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a/translated_images/yolo.a2648ec82ee8bb4ea27537677adb482fd4b733ca1705c561b6a24a85102dced5.tr.png b/translated_images/yolo.a2648ec82ee8bb4ea27537677adb482fd4b733ca1705c561b6a24a85102dced5.tr.png deleted file mode 100644 index 061350e2..00000000 Binary files a/translated_images/yolo.a2648ec82ee8bb4ea27537677adb482fd4b733ca1705c561b6a24a85102dced5.tr.png and /dev/null differ diff --git a/translations/br/README.md b/translations/br/README.md index 13dddf60..c22c79ad 100644 --- a/translations/br/README.md +++ b/translations/br/README.md @@ -1,8 +1,8 @@ -[Árabe](../ar/README.md) | [Bengali](../bn/README.md) | [Búlgaro](../bg/README.md) | [Birmanês (Myanmar)](../my/README.md) | [Chinês (Simplificado)](../zh/README.md) | [Chinês (Tradicional, Hong Kong)](../hk/README.md) | [Chinês (Tradicional, Macau)](../mo/README.md) | [Chinês (Tradicional, Taiwan)](../tw/README.md) | [Croata](../hr/README.md) | [Tcheco](../cs/README.md) | [Dinamarquês](../da/README.md) | [Holandês](../nl/README.md) | [Estoniano](../et/README.md) | [Finlandês](../fi/README.md) | [Francês](../fr/README.md) | [Alemão](../de/README.md) | [Grego](../el/README.md) | [Hebraico](../he/README.md) | [Hindi](../hi/README.md) | [Húngaro](../hu/README.md) | [Indonésio](../id/README.md) | [Italiano](../it/README.md) | [Japonês](../ja/README.md) | [Kannada](../kn/README.md) | [Coreano](../ko/README.md) | [Lituano](../lt/README.md) | [Malaio](../ms/README.md) | [Malaiala](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Pidgin Nigeriano](../pcm/README.md) | [Norueguês](../no/README.md) | [Persa (Farsi)](../fa/README.md) | [Polonês](../pl/README.md) | [Português (Brasil)](./README.md) | [Português (Portugal)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romeno](../ro/README.md) | [Russo](../ru/README.md) | [Sérvio (Cirílico)](../sr/README.md) | [Eslovaco](../sk/README.md) | [Esloveno](../sl/README.md) | [Espanhol](../es/README.md) | [Suaíli](../sw/README.md) | [Sueco](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Tailandês](../th/README.md) | [Turco](../tr/README.md) | [Ucraniano](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamita](../vi/README.md) +[Árabe](../ar/README.md) | [Bengali](../bn/README.md) | [Búlgaro](../bg/README.md) | [Birmanês (Mianmar)](../my/README.md) | [Chinês (Simplificado)](../zh/README.md) | [Chinês (Tradicional, Hong Kong)](../hk/README.md) | [Chinês (Tradicional, Macau)](../mo/README.md) | [Chinês (Tradicional, Taiwan)](../tw/README.md) | [Croata](../hr/README.md) | [Tcheco](../cs/README.md) | [Dinamarquês](../da/README.md) | [Holandês](../nl/README.md) | [Estoniano](../et/README.md) | [Finlandês](../fi/README.md) | [Francês](../fr/README.md) | [Alemão](../de/README.md) | [Grego](../el/README.md) | [Hebraico](../he/README.md) | [Hindi](../hi/README.md) | [Húngaro](../hu/README.md) | [Indonésio](../id/README.md) | [Italiano](../it/README.md) | [Japonês](../ja/README.md) | [Kannada](../kn/README.md) | [Coreano](../ko/README.md) | [Lituano](../lt/README.md) | [Malaio](../ms/README.md) | [Malaiala](../ml/README.md) | [Marata](../mr/README.md) | [Nepali](../ne/README.md) | [Pidgin Nigeriano](../pcm/README.md) | [Norueguês](../no/README.md) | [Persa (Farsi)](../fa/README.md) | [Polonês](../pl/README.md) | [Português (Brasil)](./README.md) | [Português (Portugal)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romeno](../ro/README.md) | [Russo](../ru/README.md) | [Sérvio (Cirílico)](../sr/README.md) | [Eslovaco](../sk/README.md) | [Esloveno](../sl/README.md) | [Espanhol](../es/README.md) | [Suaíli](../sw/README.md) | [Sueco](../sv/README.md) | [Tagalo (Filipino)](../tl/README.md) | [Tâmil](../ta/README.md) | [Telugu](../te/README.md) | [Tailandês](../th/README.md) | [Turco](../tr/README.md) | [Ucraniano](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamita](../vi/README.md) > **Prefere Clonar Localmente?** -> Este repositório inclui mais de 50 traduções de idiomas, o que aumenta significativamente o tamanho do download. Para clonar sem as traduções, use o sparse checkout: +> Este repositório inclui mais de 50 traduções de idiomas que aumentam significativamente o tamanho do download. Para clonar sem traduções, use checkout esparso: > ```bash > git clone --filter=blob:none --sparse https://github.com/microsoft/AI-For-Beginners.git > cd AI-For-Beginners > git sparse-checkout set --no-cone '/*' '!translations' '!translated_images' > ``` -> Isso lhe dá tudo que você precisa para completar o curso com um download muito mais rápido. +> Isso fornece tudo o que você precisa para completar o curso com um download muito mais rápido. -**Se desejar adicionar suporte para mais idiomas de tradução, estão listados [aqui](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** +**Se desejar que idiomas adicionais sejam suportados, estão listados [aqui](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** ## Junte-se à Comunidade [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) @@ -56,50 +57,50 @@ Explore o mundo da **Inteligência Artificial** (IA) com nosso currículo de 12 **[Mapa Mental do Curso](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** -Neste currículo, você irá aprender: +Neste currículo você aprenderá: -* Diferentes abordagens para Inteligência Artificial, incluindo a "boa e velha" abordagem simbólica com **Representação do Conhecimento** e raciocínio ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). -* **Redes Neurais** e **Aprendizado Profundo**, que estão no centro da IA moderna. Ilustraremos os conceitos por trás desses tópicos importantes usando código em dois dos frameworks mais populares - [TensorFlow](http://Tensorflow.org) e [PyTorch](http://pytorch.org). -* **Arquiteturas Neurais** para trabalhar com imagens e texto. Cobriremos modelos recentes, mas talvez com poucas novidades em relação ao estado da arte. -* Abordagens menos populares de IA, como **Algoritmos Genéticos** e **Sistemas Multiagentes**. +* Diferentes abordagens da Inteligência Artificial, incluindo a abordagem "boa e velha" simbólica com **Representação do Conhecimento** e raciocínio ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). +* **Redes Neurais** e **Aprendizado Profundo**, que são o núcleo da IA moderna. Iremos ilustrar os conceitos por trás destes tópicos importantes usando código em duas das frameworks mais populares - [TensorFlow](http://Tensorflow.org) e [PyTorch](http://pytorch.org). +* **Arquiteturas Neurais** para trabalhar com imagens e texto. Cobriremos modelos recentes, mas pode estar um pouco defasado do estado da arte. +* Abordagens de IA menos populares, como **Algoritmos Genéticos** e **Sistemas Multi-Agentes**. -O que não será coberto neste currículo: +O que não será abordado neste currículo: -> [Encontre todos os recursos adicionais para este curso na nossa coleção Microsoft Learn](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) +> [Encontre todos os recursos adicionais para este curso em nossa coleção Microsoft Learn](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* Casos de negócios do uso de **IA nos Negócios**. Considere fazer o caminho de aprendizado [Introdução à IA para usuários de negócios](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) na Microsoft Learn, ou a [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), desenvolvida em cooperação com a [INSEAD](https://www.insead.edu/). -* **Aprendizado de Máquina Clássico**, que está bem descrito no nosso [Currículo de Aprendizado de Máquina para Iniciantes](http://github.com/Microsoft/ML-for-Beginners). -* Aplicações práticas de IA construídas usando **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. Para isso, recomendamos começar com módulos da Microsoft Learn para [visão](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [processamento de linguagem natural](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[IA Generativa com Azure OpenAI Service](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** e outros. -* **Frameworks de Nuvem específicos para ML**, como [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum), [Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum), ou [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Considere usar os caminhos de aprendizado [Construir e operar soluções de aprendizado de máquina com Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) e [Construir e operar soluções de aprendizado de máquina com Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum). -* **IA Conversacional** e **Chat Bots**. Existe um caminho de aprendizado separado [Criar soluções de IA conversacional](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum), e você também pode consultar [este post no blog](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) para mais detalhes. -* **Matemática Profunda** por trás do aprendizado profundo. Para isso, recomendamos [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) de Ian Goodfellow, Yoshua Bengio e Aaron Courville, que também está disponível online em [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). +* Casos de uso de **IA nos Negócios**. Considere fazer o caminho de aprendizado [Introdução à IA para usuários de negócios](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) no Microsoft Learn ou a [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), criada em cooperação com a [INSEAD](https://www.insead.edu/). +* **Aprendizado de Máquina Clássico**, que é bem descrito em nosso [Currículo de Aprendizado de Máquina para Iniciantes](http://github.com/Microsoft/ML-for-Beginners). +* Aplicações práticas de IA construídas utilizando **[Serviços Cognitivos](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. Para isso, recomendamos que você comece com os módulos do Microsoft Learn para [visão](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [processamento de linguagem natural](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[IA Generativa com Azure OpenAI Service](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** e outros. +* **Frameworks de ML específicos em Nuvem**, como [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum), [Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum) ou [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Considere usar os caminhos de aprendizado [Criar e operar soluções de aprendizado de máquina com Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) e [Criar e Operar Soluções de Aprendizado de Máquina com Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum). +* **IA Conversacional** e **Chat Bots**. Há um caminho de aprendizado separado [Criar soluções de IA conversacional](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum), e você também pode consultar [este post no blog](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) para mais detalhes. +* **Matemática Avançada** por trás do aprendizado profundo. Para isso, recomendamos o livro [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) de Ian Goodfellow, Yoshua Bengio e Aaron Courville, que também está disponível online em [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). -Para uma introdução suave aos tópicos de _IA na Nuvem_, você pode considerar cursar o caminho de aprendizado [Começando com inteligência artificial no Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum). +Para uma introdução suave aos tópicos de _IA na Nuvem_ você pode considerar fazer o caminho de aprendizado [Comece com inteligência artificial no Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum). # Conteúdo -| | Link da Aula | PyTorch/Keras/TensorFlow | Laboratório | +| | Link da Lição | PyTorch/Keras/TensorFlow | Lab | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | -| 0 | [Configuração do Curso](./lessons/0-course-setup/setup.md) | [Configure seu Ambiente de Desenvolvimento](./lessons/0-course-setup/how-to-run.md) | | +| 0 | [Configuração do Curso](./lessons/0-course-setup/setup.md) | [Configure Seu Ambiente de Desenvolvimento](./lessons/0-course-setup/how-to-run.md) | | | I | [**Introdução à IA**](./lessons/1-Intro/README.md) | | | | 01 | [Introdução e História da IA](./lessons/1-Intro/README.md) | - | - | | II | **IA Simbólica** | -| 02 | [Representação do Conhecimento e Sistemas Especialistas](./lessons/2-Symbolic/README.md) | [Sistemas Especialistas](./lessons/2-Symbolic/Animals.ipynb) / [Ontologia](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Grafo de Conceitos](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | +| 02 | [Representação do Conhecimento e Sistemas Especialistas](./lessons/2-Symbolic/README.md) | [Sistemas Especialistas](./lessons/2-Symbolic/Animals.ipynb) / [Ontologia](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Grafo Conceitual](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | | III | [**Introdução às Redes Neurais**](./lessons/3-NeuralNetworks/README.md) ||| | 03 | [Perceptron](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [Notebook](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [Lab](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | | 04 | [Perceptron Multicamadas e Criando nosso próprio Framework](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Notebook](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Lab](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | | 05 | [Introdução a Frameworks (PyTorch/TensorFlow) e Overfitting](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [PyTorch](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [Keras](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | -| IV | [**Visão Computacional**](./lessons/4-ComputerVision/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [Explore a Visão Computacional no Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | +| IV | [**Visão Computacional**](./lessons/4-ComputerVision/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [Explore Visão Computacional no Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | | 06 | [Introdução à Visão Computacional. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Notebook](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Lab](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | | 07 | [Redes Neurais Convolucionais](./lessons/4-ComputerVision/07-ConvNets/README.md) & [Arquiteturas CNN](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [Lab](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | -| 08 | [Redes Pré-treinadas e Transfer Learning](./lessons/4-ComputerVision/08-TransferLearning/README.md) e [Dicas de Treinamento](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | +| 08 | [Redes Pré-Treinadas e Transfer Learning](./lessons/4-ComputerVision/08-TransferLearning/README.md) e [Dicas de Treinamento](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | | 09 | [Autoencoders e VAEs](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | -| 10 | [Redes Generativas Adversariais & Transferência de Estilo Artístico](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | +| 10 | [Redes Generativas Adversariais e Transferência de Estilo Artístico](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | | 11 | [Detecção de Objetos](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [Lab](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | | 12 | [Segmentação Semântica. U-Net](./lessons/4-ComputerVision/12-Segmentation/README.md) | [PyTorch](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb) | | | V | [**Processamento de Linguagem Natural**](./lessons/5-NLP/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [Explore Processamento de Linguagem Natural no Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| -| 13 | [Representação de Texto. Bag of Words/TF-IDF](./lessons/5-NLP/13-TextRep/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb) | | -| 14 | [Embeddings semânticos de palavras. Word2Vec e GloVe](./lessons/5-NLP/14-Embeddings/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb) | | +| 13 | [Representação de Texto. Bow/TF-IDF](./lessons/5-NLP/13-TextRep/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb) | | +| 14 | [Word embeddings semânticos. Word2Vec e GloVe](./lessons/5-NLP/14-Embeddings/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb) | | | 15 | [Modelagem de Linguagem. Treinando seus próprios embeddings](./lessons/5-NLP/15-LanguageModeling/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [Lab](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | | 16 | [Redes Neurais Recorrentes](./lessons/5-NLP/16-RNN/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNTF.ipynb) | | | 17 | [Redes Recorrentes Generativas](./lessons/5-NLP/17-GenerativeNetworks/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb) | [Lab](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | @@ -117,57 +118,57 @@ Para uma introdução suave aos tópicos de _IA na Nuvem_, você pode considerar ## Cada lição contém -* Material para leitura prévia -* Notebooks Jupyter executáveis, frequentemente específicos para o framework (**PyTorch** ou **TensorFlow**). O notebook executável também contém muito material teórico, portanto, para entender o tópico, você precisa passar por pelo menos uma versão do notebook (PyTorch ou TensorFlow). -* **Labs** disponíveis para alguns tópicos, que te dão a oportunidade de tentar aplicar o material aprendido a um problema específico. -* Algumas seções contêm links para módulos do [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) que abordam tópicos relacionados. +* Material de leitura prévia +* Jupyter Notebooks executáveis, frequentemente específicos para o framework (**PyTorch** ou **TensorFlow**). O notebook executável também contém muito material teórico, então para entender o tema é preciso passar por pelo menos uma versão do notebook (ou PyTorch ou TensorFlow). +* **Labs** disponíveis para alguns tópicos, que oferecem a oportunidade de aplicar o material aprendido em um problema específico. +* Algumas seções contêm links para módulos do [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) que cobrem tópicos relacionados. ## Começando ### 🎯 Novo em IA? Comece aqui! -Se você é totalmente novo em IA e quer exemplos rápidos e práticos, confira nossos [**Exemplos para Iniciantes**](./examples/README.md)! Eles incluem: +Se você é completamente novo em IA e quer exemplos rápidos e práticos, confira nossos [**Exemplos para Iniciantes**](./examples/README.md)! Estes incluem: - 🌟 **Olá Mundo IA** - Seu primeiro programa de IA (reconhecimento de padrões) - 🧠 **Rede Neural Simples** - Construa uma rede neural do zero - 🖼️ **Classificador de Imagens** - Classifique imagens com comentários detalhados -- 💬 **Sentimento do Texto** - Analise texto positivo/negativo +- 💬 **Sentimento de Texto** - Analise texto positivo/negativo -Estes exemplos foram projetados para ajudar você a entender conceitos de IA antes de mergulhar no currículo completo. +Estes exemplos foram criados para ajudá-lo a entender conceitos de IA antes de mergulhar no currículo completo. ### 📚 Configuração do Currículo Completo -- Criamos uma [aula de configuração](./lessons/0-course-setup/setup.md) para ajudar você a configurar seu ambiente de desenvolvimento. - Para Educadores, também criamos uma [aula de configuração do currículo](./lessons/0-course-setup/for-teachers.md)! -- Como [Executar o código em um VSCode ou um Codepace](./lessons/0-course-setup/how-to-run.md) +- Criamos uma [lição de configuração](./lessons/0-course-setup/setup.md) para ajudar você a configurar seu ambiente de desenvolvimento. - Para Educadores, também criamos uma [lição de configuração do currículo](./lessons/0-course-setup/for-teachers.md)! +- Como [Executar o código no VSCode ou Codespace](./lessons/0-course-setup/how-to-run.md) -Siga estas etapas: +Siga estes passos: Fork do Repositório: Clique no botão "Fork" no canto superior direito desta página. Clone o Repositório: `git clone https://github.com/microsoft/AI-For-Beginners.git` -Não esqueça de dar estrela (🌟) neste repositório para encontrá-lo mais facilmente depois. +Não se esqueça de dar uma estrela (🌟) neste repositório para encontrá-lo mais facilmente depois. ## Conheça outros Aprendizes -Junte-se ao nosso [servidor oficial de Discord de IA](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) para conhecer e se conectar com outros aprendizes que estão fazendo este curso e obter suporte. +Junte-se ao nosso [servidor oficial de IA no Discord](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) para conhecer e fazer networking com outros aprendizes deste curso e obter suporte. -Se você tiver feedback de produto ou dúvidas durante a construção, visite nosso [Fórum de Desenvolvedores Azure AI Foundry](https://aka.ms/foundry/forum) +Se você tiver feedback sobre o produto ou dúvidas durante a construção, visite nosso [Fórum de Desenvolvedores Azure AI Foundry](https://aka.ms/foundry/forum) ## Questionários -> **Uma nota sobre questionários**: Todos os questionários estão contidos na pasta Quiz-app em etc\quiz-app, ou [Online Aqui](https://ff-quizzes.netlify.app/) Eles são linkados dentro das aulas; o app de questionários pode ser executado localmente ou implantado no Azure; siga as instruções na pasta `quiz-app`. Eles estão sendo progressivamente localizados. +> **Uma nota sobre questionários**: Todos os questionários estão contidos na pasta Quiz-app em etc\quiz-app, ou [Online Aqui](https://ff-quizzes.netlify.app/) Eles estão interligados dentro das lições e o aplicativo de questionário pode ser executado localmente ou implantado no Azure; siga as instruções na pasta `quiz-app`. Eles estão sendo gradualmente localizados. ## Ajuda Necessária -Você tem sugestões ou encontrou erros de ortografia ou no código? Abra uma issue ou crie um pull request. +Você tem sugestões ou encontrou erros de ortografia ou código? Abra uma issue ou crie um pull request. ## Agradecimentos Especiais * **✍️ Autor Principal:** [Dmitry Soshnikov](http://soshnikov.com), PhD * **🔥 Editor:** [Jen Looper](https://twitter.com/jenlooper), PhD -* **🎨 Ilustrador de Sketchnote:** [Tomomi Imura](https://twitter.com/girlie_mac) -* **✅ Criador de Questionários:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) +* **🎨 Ilustradora das Sketchnotes:** [Tomomi Imura](https://twitter.com/girlie_mac) +* **✅ Criadora dos Questionários:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) * **🙏 Contribuidores Principais:** [Evgenii Pishchik](https://github.com/Pe4enIks) ## Outros Currículos @@ -188,7 +189,7 @@ Nossa equipe produz outros currículos! Confira: [![AI Agents for Beginners](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst) --- - + ### Série de IA Generativa [![Generative AI for Beginners](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst) [![Generative AI (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst) @@ -196,8 +197,8 @@ Nossa equipe produz outros currículos! Confira: [![Generative AI (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst) --- - -### Aprendizado Básico + +### Aprendizado Central [![ML for Beginners](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) [![Data Science for Beginners](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst) [![AI for Beginners](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst) @@ -207,7 +208,7 @@ Nossa equipe produz outros currículos! Confira: [![XR Development for Beginners](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst) --- - + ### Série Copilot [![Copilot for AI Paired Programming](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) [![Copilot for C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst) @@ -216,7 +217,7 @@ Nossa equipe produz outros currículos! Confira: ## Obtendo Ajuda -Se você ficar preso ou tiver quaisquer dúvidas sobre como criar aplicativos de IA. Junte-se a outros aprendizes e desenvolvedores experientes em discussões sobre MCP. É uma comunidade solidária onde perguntas são bem-vindas e o conhecimento é compartilhado livremente. +Se você ficar preso ou tiver qualquer dúvida sobre construir aplicativos de IA, junte-se a outros aprendizes e desenvolvedores experientes em discussões sobre MCP. É uma comunidade de suporte onde perguntas são bem-vindas e o conhecimento é compartilhado livremente. [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) @@ -228,5 +229,5 @@ Se você tiver feedback sobre o produto ou erros durante a construção, visite: **Aviso Legal**: -Este documento foi traduzido utilizando o serviço de tradução automática [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automatizadas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte autorizada. Para informações críticas, recomenda-se tradução profissional humana. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações incorretas decorrentes do uso desta tradução. +Este documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automatizadas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte autorizada. Para informações críticas, recomenda-se tradução profissional humana. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações incorretas decorrentes do uso desta tradução. \ No newline at end of file diff --git a/translations/br/lessons/0-course-setup/how-to-run.md b/translations/br/lessons/0-course-setup/how-to-run.md index a4f84e51..0cf291eb 100644 --- a/translations/br/lessons/0-course-setup/how-to-run.md +++ b/translations/br/lessons/0-course-setup/how-to-run.md @@ -1,21 +1,21 @@ # Como Executar o Código -Este curso contém muitos exemplos executáveis e laboratórios que você provavelmente vai querer rodar. Para isso, é necessário ter a capacidade de executar código Python em Jupyter Notebooks fornecidos como parte deste curso. Você tem várias opções para executar o código: +Este currículo contém muitos exemplos executáveis e laboratórios que você vai querer executar. Para isso, você precisa da capacidade de executar código Python nos Jupyter Notebooks fornecidos como parte deste currículo. Você tem várias opções para executar o código: ## Executar localmente no seu computador -Para executar o código localmente no seu computador, você precisará ter alguma versão do Python instalada. Eu recomendo instalar o **[miniconda](https://conda.io/en/latest/miniconda.html)** - é uma instalação leve que suporta o gerenciador de pacotes `conda` para diferentes **ambientes virtuais** Python. +Para executar o código localmente no seu computador, é necessária uma instalação do Python. Uma recomendação é instalar o **[miniconda](https://conda.io/en/latest/miniconda.html)** - é uma instalação bastante leve que suporta o gerenciador de pacotes `conda` para diferentes **ambientes virtuais** Python. -Depois de instalar o miniconda, você precisará clonar o repositório e criar um ambiente virtual para ser usado neste curso: +Depois de instalar o miniconda, clone o repositório e crie um ambiente virtual para ser usado neste curso: ```bash git clone http://github.com/microsoft/ai-for-beginners @@ -24,19 +24,19 @@ conda env create --name ai4beg --file .devcontainer/environment.yml conda activate ai4beg ``` -### Usando o Visual Studio Code com a Extensão Python +### Usando Visual Studio Code com Extensão Python -Provavelmente, a melhor maneira de usar o curso é abri-lo no [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) com a [Extensão Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste). +Este currículo é melhor utilizado ao abri-lo no [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) com a [Extensão Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste). -> **Nota**: Assim que você clonar e abrir o diretório no VS Code, ele automaticamente sugerirá a instalação das extensões Python. Você também precisará instalar o miniconda conforme descrito acima. +> **Nota**: Após clonar e abrir o diretório no VS Code, ele automaticamente sugerirá a instalação das extensões Python. Você também precisará instalar o miniconda conforme descrito acima. -> **Nota**: Se o VS Code sugerir reabrir o repositório em um container, recuse para usar a instalação local do Python. +> **Nota**: Se o VS Code sugerir reabrir o repositório em um contêiner, você deve recusar para usar a instalação local do Python. -### Usando o Jupyter no Navegador +### Usando Jupyter no Navegador -Você também pode usar o ambiente Jupyter diretamente do navegador no seu próprio computador. Na verdade, tanto o Jupyter clássico quanto o Jupyter Hub oferecem um ambiente de desenvolvimento bastante conveniente com autocompletar, destaque de código, etc. +Você também pode usar um ambiente Jupyter pelo navegador em seu próprio computador. Tanto o Jupyter clássico quanto o JupyterHub fornecem um ambiente de desenvolvimento conveniente com auto-completar, realce de código, etc. -Para iniciar o Jupyter localmente, vá até o diretório do curso e execute: +Para iniciar o Jupyter localmente, vá para o diretório do curso e execute: ```bash jupyter notebook @@ -45,32 +45,36 @@ ou ```bash jupyterhub ``` -Depois, você pode navegar até qualquer um dos arquivos `.ipynb`, abri-los e começar a trabalhar. +Depois você pode navegar para qualquer arquivo `.ipynb`, abri-lo e começar a trabalhar. -### Executando em um Container +### Executando em contêiner -Uma alternativa à instalação do Python seria executar o código em um container. Como nosso repositório contém uma pasta especial `.devcontainer` que instrui como construir um container para este repositório, o VS Code oferecerá a opção de reabrir o código em um container. Isso exigirá a instalação do Docker e será mais complexo, então recomendamos essa opção para usuários mais experientes. +Uma alternativa à instalação do Python seria executar o código em um contêiner. Como nosso repositório fornece uma pasta especial `.devcontainer` que instrui como construir um contêiner para este repositório, o VS Code oferece a oportunidade de reabrir o código em um contêiner. Isso requer a instalação do Docker, e também seria mais complexo, então recomendamos isso para usuários mais experientes. ## Executando na Nuvem -Se você não quiser instalar o Python localmente e tiver acesso a alguns recursos na nuvem, uma boa alternativa seria executar o código na nuvem. Existem várias maneiras de fazer isso: +Se você não quiser instalar o Python localmente e tiver acesso a alguns recursos na nuvem - uma boa alternativa seria executar o código na nuvem. Existem várias formas de fazer isso: -* Usando o **[GitHub Codespaces](https://github.com/features/codespaces)**, que é um ambiente virtual criado para você no GitHub, acessível através da interface do navegador do VS Code. Se você tiver acesso ao Codespaces, basta clicar no botão **Code** no repositório, iniciar um codespace e começar a trabalhar rapidamente. -* Usando o **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. O [Binder](https://mybinder.org) oferece recursos computacionais gratuitos na nuvem para pessoas como você testarem algum código no GitHub. Há um botão na página inicial para abrir o repositório no Binder - isso deve levá-lo rapidamente ao site do Binder, que construirá o container subjacente e iniciará a interface web do Jupyter para você de forma transparente. +* Usando **[GitHub Codespaces](https://github.com/features/codespaces)**, que é um ambiente virtual criado para você no GitHub, acessível através da interface do navegador do VS Code. Se você tem acesso ao Codespaces, basta clicar no botão **Code** no repositório, iniciar um codespace e começar a trabalhar rapidamente. +* Usando **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. [Binder](https://mybinder.org) oferece recursos computacionais gratuitos na nuvem para pessoas como você testarem algum código no GitHub. Há um botão na página principal para abrir o repositório no Binder - isso deve levá-lo rapidamente ao site do binder, que construirá um contêiner subjacente e iniciará uma interface web Jupyter para você de forma transparente. -> **Nota**: Para evitar uso indevido, o Binder bloqueia o acesso a alguns recursos da web. Isso pode impedir que alguns códigos funcionem, especialmente aqueles que baixam modelos e/ou conjuntos de dados da Internet pública. Você pode precisar encontrar alternativas. Além disso, os recursos computacionais fornecidos pelo Binder são bastante básicos, então o treinamento será lento, especialmente nas lições mais complexas. +> **Nota**: Para evitar uso indevido, o Binder tem acesso a alguns recursos web bloqueado. Isso pode impedir o funcionamento de algum código que busca modelos e/ou conjuntos de dados na internet pública. Você pode precisar encontrar algumas soluções alternativas. Além disso, os recursos computacionais oferecidos pelo Binder são bastante básicos, então o treinamento será lento, especialmente em aulas mais complexas. ## Executando na Nuvem com GPU -Algumas das lições mais avançadas deste curso se beneficiariam muito do suporte a GPU, pois, caso contrário, o treinamento será extremamente lento. Existem algumas opções que você pode seguir, especialmente se tiver acesso à nuvem, seja através do [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) ou da sua instituição: +Algumas das aulas posteriores deste currículo se beneficiariam muito do suporte a GPU. O treinamento de modelos, por exemplo, pode ser dolorosamente lento sem isso. Existem algumas opções que você pode seguir, especialmente se tiver acesso à nuvem pela [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) ou pela sua instituição: -* Criar uma [Máquina Virtual de Ciência de Dados](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) e conectá-la via Jupyter. Você pode então clonar o repositório diretamente na máquina e começar a aprender. As VMs da série NC possuem suporte a GPU. +* Crie uma [Máquina Virtual de Ciência de Dados](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) e conecte-se a ela via Jupyter. Você pode então clonar o repositório diretamente na máquina e começar a aprender. VMs da série NC têm suporte a GPU. -> **Nota**: Algumas assinaturas, incluindo o Azure for Students, não oferecem suporte a GPU por padrão. Pode ser necessário solicitar núcleos de GPU adicionais através de um pedido de suporte técnico. +> **Nota**: Algumas assinaturas, incluindo Azure for Students, não oferecem suporte a GPU por padrão. Você pode precisar solicitar núcleos GPU adicionais por meio de um pedido de suporte técnico. -* Criar um [Workspace do Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) e usar o recurso de Notebook lá. [Este vídeo](https://azure-for-academics.github.io/quickstart/azureml-papers/) mostra como clonar um repositório em um notebook do Azure ML e começar a usá-lo. +* Crie um [Workspace do Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) e use o recurso Notebook nele. [Este vídeo](https://azure-for-academics.github.io/quickstart/azureml-papers/) mostra como clonar um repositório dentro do notebook Azure ML e começar a usar. -Você também pode usar o Google Colab, que oferece algum suporte gratuito a GPU, e carregar os Jupyter Notebooks lá para executá-los um por um. +Você também pode usar o Google Colab, que oferece algum suporte gratuito a GPU, e fazer upload dos Jupyter Notebooks para executá-los um por um. +--- + + **Aviso Legal**: -Este documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automáticas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte oficial. Para informações críticas, recomenda-se a tradução profissional feita por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações equivocadas decorrentes do uso desta tradução. \ No newline at end of file +Este documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automáticas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte autoritativa. Para informações críticas, recomendamos a tradução profissional realizada por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações incorretas decorrentes do uso desta tradução. + \ No newline at end of file diff --git a/translations/br/lessons/2-Symbolic/Animals.ipynb b/translations/br/lessons/2-Symbolic/Animals.ipynb index 4f0cd96a..dcc9230e 100644 --- a/translations/br/lessons/2-Symbolic/Animals.ipynb +++ b/translations/br/lessons/2-Symbolic/Animals.ipynb @@ -6,13 +6,13 @@ "collapsed": true }, "source": [ - "# Implementando um Sistema Especialista em Animais\n", + "# Implementando um Sistema Especialista de Animais\n", "\n", - "Um exemplo do [Currículo de IA para Iniciantes](http://github.com/microsoft/ai-for-beginners).\n", + "Um exemplo do [Currículo AI para Iniciantes](http://github.com/microsoft/ai-for-beginners).\n", "\n", - "Neste exemplo, vamos implementar um sistema simples baseado em conhecimento para determinar um animal com base em algumas características físicas. O sistema pode ser representado pela seguinte árvore AND-OR (esta é uma parte da árvore completa, podemos facilmente adicionar mais regras):\n", + "Neste exemplo, implementaremos um sistema baseado em conhecimento simples para determinar um animal com base em algumas características físicas. O sistema pode ser representado pela seguinte árvore AND-OR (esta é uma parte da árvore inteira, podemos facilmente adicionar mais algumas regras):\n", "\n", - "![](../../../../translated_images/br/AND-OR-Tree.5592d2c70187f283.webp)\n" + "![](../../../../../../translated_images/br/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { @@ -21,10 +21,10 @@ "source": [ "## Nosso próprio shell de sistemas especialistas com inferência reversa\n", "\n", - "Vamos tentar definir uma linguagem simples para representação de conhecimento baseada em regras de produção. Usaremos classes Python como palavras-chave para definir regras. Basicamente, haverá 3 tipos de classes:\n", + "Vamos tentar definir uma linguagem simples para representação de conhecimento baseada em regras de produção. Usaremos classes do Python como palavras-chave para definir as regras. Basicamente, haverá 3 tipos de classes:\n", "* `Ask` representa uma pergunta que precisa ser feita ao usuário. Ela contém o conjunto de respostas possíveis.\n", - "* `If` representa uma regra, e é apenas uma forma sintática para armazenar o conteúdo da regra.\n", - "* `AND`/`OR` são classes para representar os ramos AND/OR da árvore. Elas apenas armazenam a lista de argumentos internamente. Para simplificar o código, toda a funcionalidade é definida na classe pai `Content`.\n" + "* `If` representa uma regra, e é apenas um açúcar sintático para armazenar o conteúdo da regra\n", + "* `AND`/`OR` são classes para representar ramos E/OU da árvore. Eles apenas armazenam a lista de argumentos dentro. Para simplificar o código, toda funcionalidade é definida na classe pai `Content`\n" ] }, { @@ -66,7 +66,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Em nosso sistema, a memória de trabalho conteria a lista de **fatos** como **pares atributo-valor**. A base de conhecimento pode ser definida como um grande dicionário que mapeia ações (novos fatos que devem ser inseridos na memória de trabalho) para condições, expressas como expressões AND-OR. Além disso, alguns fatos podem ser `Perguntados`.\n" + "Em nosso sistema, a memória de trabalho conteria a lista de **fatos** como **pares atributo-valor**. A base de conhecimento pode ser definida como um grande dicionário que associa ações (novos fatos que devem ser inseridos na memória de trabalho) a condições, expressas como expressões AND-OR. Além disso, alguns fatos podem ser `Perguntados`.\n" ] }, { @@ -101,11 +101,11 @@ "source": [ "Para realizar a inferência reversa, definiremos a classe `Knowledgebase`. Ela conterá:\n", "* `memory` de trabalho - um dicionário que mapeia atributos para valores\n", - "* `rules` da base de conhecimento no formato definido acima\n", + "* `rules` do Knowledgebase no formato definido acima\n", "\n", - "Os dois métodos principais são:\n", - "* `get` para obter o valor de um atributo, realizando a inferência, se necessário. Por exemplo, `get('color')` obteria o valor de um slot de cor (ele perguntará, se necessário, e armazenará o valor para uso posterior na memória de trabalho). Se pedirmos `get('color:blue')`, ele perguntará por uma cor e, em seguida, retornará o valor `y`/`n` dependendo da cor.\n", - "* `eval` realiza a inferência propriamente dita, ou seja, percorre a árvore AND/OR, avalia subobjetivos, etc.\n" + "Dois métodos principais são:\n", + "* `get` para obter o valor de um atributo, realizando a inferência se necessário. Por exemplo, `get('color')` obteria o valor de um slot de cor (ele perguntará se necessário e armazenará o valor para uso posterior na memória de trabalho). Se perguntarmos `get('color:blue')`, ele perguntará pela cor e então retornará o valor `y`/`n` dependendo da cor.\n", + "* `eval` realiza a inferência real, ou seja, percorre a árvore AND/OR, avalia sub-objetivos etc.\n" ] }, { @@ -172,7 +172,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Agora vamos definir nossa base de conhecimento sobre animais e realizar a consulta. Observe que esta chamada fará perguntas a você. Você pode responder digitando `s`/`n` para perguntas de sim-não, ou especificando um número (0..N) para perguntas com respostas de múltipla escolha mais longas.\n" + "Agora vamos definir nossa base de conhecimento sobre animais e realizar a consulta. Note que esta chamada fará perguntas a você. Você pode responder digitando `y`/`n` para perguntas de sim ou não, ou especificando um número (0..N) para perguntas com respostas múltipla escolha mais longas.\n" ] }, { @@ -229,11 +229,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Usando PyKnow para Inferência Progressiva\n", + "## Usando Experta para Inferência Direta\n", "\n", - "No próximo exemplo, tentaremos implementar a inferência progressiva usando uma das bibliotecas para representação de conhecimento, [PyKnow](https://github.com/buguroo/pyknow/). **PyKnow** é uma biblioteca para criar sistemas de inferência progressiva em Python, projetada para ser semelhante ao sistema clássico antigo [CLIPS](http://www.clipsrules.net/index.html).\n", + "No próximo exemplo, tentaremos implementar inferência direta usando uma das bibliotecas para representação de conhecimento, [Experta](https://github.com/nilp0inter/experta). **Experta** é uma biblioteca para criação de sistemas de inferência direta em Python, que foi projetada para ser semelhante ao clássico sistema antigo [CLIPS](http://www.clipsrules.net/index.html).\n", "\n", - "Também poderíamos ter implementado o encadeamento progressivo por conta própria sem muitos problemas, mas implementações ingênuas geralmente não são muito eficientes. Para um casamento de regras mais eficaz, é usado um algoritmo especial chamado [Rete](https://en.wikipedia.org/wiki/Rete_algorithm).\n" + "Também poderíamos ter implementado encadeamento para frente nós mesmos sem muitos problemas, mas implementações ingênuas geralmente não são muito eficientes. Para uma correspondência de regras mais eficaz, um algoritmo especial [Rete](https://en.wikipedia.org/wiki/Rete_algorithm) é usado.\n" ] }, { @@ -247,32 +247,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "Collecting git+https://github.com/buguroo/pyknow/\n", - " Cloning https://github.com/buguroo/pyknow/ to /tmp/pip-req-build-3cqeulyl\n", - " Running command git clone --filter=blob:none --quiet https://github.com/buguroo/pyknow/ /tmp/pip-req-build-3cqeulyl\n", - " Resolved https://github.com/buguroo/pyknow/ to commit 48818336f2e9a126f1964f2d8dc22d37ff800fe8\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting frozendict==1.2\n", - " Using cached frozendict-1.2.tar.gz (2.6 kB)\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting schema==0.6.7\n", - " Using cached schema-0.6.7-py2.py3-none-any.whl (14 kB)\n", - "Building wheels for collected packages: pyknow, frozendict\n", - " Building wheel for pyknow (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for pyknow: filename=pyknow-1.7.0-py3-none-any.whl size=34228 sha256=b7de5b09292c4007667c72f69b98d5a1b5f7324ff15f9dd8e077c3d5f7aade42\n", - " Stored in directory: /tmp/pip-ephem-wheel-cache-k7jpave7/wheels/81/1a/d3/f6c15dbe1955598a37755215f2a10449e7418500d7bd4b9508\n", - " Building wheel for frozendict (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for frozendict: filename=frozendict-1.2-py3-none-any.whl size=3148 sha256=2863d55c240d2409cddf05ccfe600591f8478681549fc97555c47c90dc6bb160\n", - " Stored in directory: /home/rg/.cache/pip/wheels/49/ac/f8/cb8120244e710bdb479c86198b03c7b08c3c2d3d2bf448fd6e\n", - "Successfully built pyknow frozendict\n", - "Installing collected packages: schema, frozendict, pyknow\n", - "Successfully installed frozendict-1.2 pyknow-1.7.0 schema-0.6.7\n" + "Collecting git+https://github.com/nilp0inter/experta\n", + " Cloning https://github.com/nilp0inter/experta to /tmp/pip-req-build-7qurtwk3\n", + " Running command git clone --filter=blob:none --quiet https://github.com/nilp0inter/experta /tmp/pip-req-build-7qurtwk3\n", + " Resolved https://github.com/nilp0inter/experta to commit c6d5834b123861f5ae09e7d07027dc98bec58741\n", + " Installing build dependencies ... \u001b[?25ldone\n", + "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", + "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25hRequirement already satisfied: frozendict~=2.4.6 in /opt/conda/envs/ai4beg/lib/python3.12/site-packages (from experta==1.9.5.dev1) (2.4.7)\n", + "Collecting schema~=0.6.7 (from experta==1.9.5.dev1)\n", + " Downloading schema-0.6.8-py2.py3-none-any.whl.metadata (14 kB)\n", + "Downloading schema-0.6.8-py2.py3-none-any.whl (14 kB)\n", + "Building wheels for collected packages: experta\n", + " Building wheel for experta (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25h Created wheel for experta: filename=experta-1.9.5.dev1-py3-none-any.whl size=34804 sha256=888c459512a5e713f4b674caa9a0f96cfdf07ec0d6eb56cc318ce0653d218014\n", + " Stored in directory: /tmp/pip-ephem-wheel-cache-1eeii9zy/wheels/3d/e8/bb/22d7956359603fa8dd679aa09f5b8efb3f29991c3986fdc787\n", + "Successfully built experta\n", + "Installing collected packages: schema, experta\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2/2\u001b[0m [experta]\n", + "\u001b[1A\u001b[2KSuccessfully installed experta-1.9.5.dev1 schema-0.6.8\n" ] } ], "source": [ "import sys\n", - "!{sys.executable} -m pip install git+https://github.com/buguroo/pyknow/" + "!{sys.executable} -m pip install git+https://github.com/nilp0inter/experta" ] }, { @@ -283,15 +282,15 @@ }, "outputs": [], "source": [ - "from pyknow import *\n", - "#import pyknow" + "from experta import *\n", + "#import experta" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Definiremos nosso sistema como uma classe que herda de `KnowledgeEngine`. Cada regra é definida por uma função separada com a anotação `@Rule`, que especifica quando a regra deve ser acionada. Dentro da regra, podemos adicionar novos fatos usando a função `declare`, e adicionar esses fatos resultará em mais regras sendo chamadas pelo mecanismo de inferência direta.\n" + "Nós definiremos nosso sistema como uma classe que herda de `KnowledgeEngine`. Cada regra é definida por uma função separada com a anotação `@Rule`, que especifica quando a regra deve ser acionada. Dentro da regra, podemos adicionar novos fatos usando a função `declare`, e a adição desses fatos resultará na chamada de mais regras pelo motor de inferência direta.\n" ] }, { @@ -378,7 +377,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Uma vez que definimos uma base de conhecimento, populamos nossa memória de trabalho com alguns fatos iniciais e, em seguida, chamamos o método `run()` para realizar a inferência. Você pode ver, como resultado, que novos fatos inferidos são adicionados à memória de trabalho, incluindo o fato final sobre o animal (se configurarmos todos os fatos iniciais corretamente).\n" + "Uma vez que definimos uma base de conhecimento, populamos nossa memória de trabalho com alguns fatos iniciais e então chamamos o método `run()` para realizar a inferência. Você pode ver como resultado que novos fatos inferidos são adicionados à memória de trabalho, incluindo o fato final sobre o animal (se configurarmos todos os fatos iniciais corretamente).\n" ] }, { @@ -440,7 +439,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n---\n\n**Aviso Legal**: \nEste documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automáticas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte oficial. Para informações críticas, recomenda-se a tradução profissional feita por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações equivocadas decorrentes do uso desta tradução.\n" + "---\n\n\n**Aviso Legal**: \nEste documento foi traduzido utilizando o serviço de tradução automática [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automáticas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte oficial. Para informações críticas, recomenda-se a tradução profissional humana. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações equivocadas decorrentes do uso desta tradução.\n\n" ] } ], @@ -467,8 +466,8 @@ "version": "3.11.2" }, "coopTranslator": { - "original_hash": "ab2bd97b0453415b89a469284609a8ce", - "translation_date": "2025-08-28T13:34:41+00:00", + "original_hash": "8ef43db4b9182239fd150a76bd494fdb", + "translation_date": "2026-01-15T14:03:48+00:00", "source_file": "lessons/2-Symbolic/Animals.ipynb", "language_code": "br" } diff --git a/translations/br/lessons/2-Symbolic/README.md b/translations/br/lessons/2-Symbolic/README.md index 4a5f3d02..fbe088e8 100644 --- a/translations/br/lessons/2-Symbolic/README.md +++ b/translations/br/lessons/2-Symbolic/README.md @@ -1,116 +1,116 @@ -# Representação de Conhecimento e Sistemas Especialistas +# Representação do Conhecimento e Sistemas Especialistas -![Resumo do conteúdo de IA Simbólica](../../../../translated_images/br/ai-symbolic.715a30cb610411a6.webp) +![Resumo do conteúdo de IA Simbólica](../../../../../../translated_images/br/ai-symbolic.715a30cb610411a6.webp) > Sketchnote por [Tomomi Imura](https://twitter.com/girlie_mac) -A busca pela inteligência artificial é baseada na procura por conhecimento, para compreender o mundo de forma semelhante aos humanos. Mas como isso pode ser feito? +A busca pela inteligência artificial é baseada na busca pelo conhecimento, para compreender o mundo de forma semelhante aos humanos. Mas como você pode fazer isso? ## [Quiz pré-aula](https://ff-quizzes.netlify.app/en/ai/quiz/3) -Nos primeiros dias da IA, a abordagem de cima para baixo para criar sistemas inteligentes (discutida na aula anterior) era popular. A ideia era extrair o conhecimento das pessoas em uma forma legível por máquinas e, então, usá-lo para resolver problemas automaticamente. Essa abordagem era baseada em duas grandes ideias: +Nos primeiros dias da IA, a abordagem top-down para criar sistemas inteligentes (discutida na lição anterior) era popular. A ideia era extrair o conhecimento das pessoas para alguma forma legível por máquina e então usá-lo para resolver problemas automaticamente. Essa abordagem foi baseada em duas grandes ideias: -* Representação de Conhecimento +* Representação do Conhecimento * Raciocínio -## Representação de Conhecimento +## Representação do Conhecimento -Um dos conceitos importantes na IA Simbólica é o **conhecimento**. É essencial diferenciar conhecimento de *informação* ou *dados*. Por exemplo, pode-se dizer que livros contêm conhecimento, porque ao estudá-los podemos nos tornar especialistas. No entanto, o que os livros realmente contêm é chamado de *dados*, e ao ler os livros e integrar esses dados em nosso modelo do mundo, transformamos os dados em conhecimento. +Um dos conceitos importantes na IA Simbólica é o **conhecimento**. É importante diferenciar conhecimento de *informação* ou *dados*. Por exemplo, pode-se dizer que livros contêm conhecimento, porque é possível estudar os livros e tornar-se um especialista. No entanto, o que os livros contêm é na verdade chamado de *dados*, e ao ler livros e integrar esses dados em nosso modelo do mundo, nós convertemos esses dados em conhecimento. -> ✅ **Conhecimento** é algo que está em nossa mente e representa nossa compreensão do mundo. Ele é obtido por um processo ativo de **aprendizado**, que integra pedaços de informação que recebemos ao nosso modelo ativo do mundo. +> ✅ **Conhecimento** é algo que está contido em nossa cabeça e representa nossa compreensão do mundo. É obtido por um processo ativo de **aprendizagem**, que integra pedaços de informação que recebemos em nosso modelo ativo do mundo. -Na maioria das vezes, não definimos estritamente o conhecimento, mas o alinhamos com outros conceitos relacionados usando o [DIKW Pyramid](https://en.wikipedia.org/wiki/DIKW_pyramid). Ele contém os seguintes conceitos: +Na maioria das vezes, não definimos conhecimento de forma estrita, mas o alinhamos com outros conceitos relacionados usando a [Pirâmide DIKW](https://en.wikipedia.org/wiki/DIKW_pyramid). Ela contém os seguintes conceitos: -* **Dados** são algo representado em mídia física, como texto escrito ou palavras faladas. Os dados existem independentemente dos seres humanos e podem ser transmitidos entre pessoas. -* **Informação** é como interpretamos os dados em nossa mente. Por exemplo, ao ouvir a palavra *computador*, temos alguma compreensão do que é. -* **Conhecimento** é a informação integrada ao nosso modelo do mundo. Por exemplo, uma vez que aprendemos o que é um computador, começamos a ter ideias sobre como ele funciona, quanto custa e para que pode ser usado. Essa rede de conceitos inter-relacionados forma nosso conhecimento. -* **Sabedoria** é um nível ainda mais elevado de nossa compreensão do mundo e representa o *meta-conhecimento*, ou seja, uma noção de como e quando o conhecimento deve ser usado. +* **Dados** são algo representado em mídia física, como texto escrito ou palavras faladas. Dados existem independentemente dos seres humanos e podem ser passados entre as pessoas. +* **Informação** é como interpretamos dados em nossa mente. Por exemplo, quando ouvimos a palavra *computador*, temos alguma compreensão do que é. +* **Conhecimento** é a informação integrada em nosso modelo do mundo. Por exemplo, uma vez que aprendemos o que é um computador, começamos a ter algumas ideias sobre como ele funciona, quanto custa e para que pode ser usado. Essa rede de conceitos inter-relacionados forma nosso conhecimento. +* **Sabedoria** é ainda um nível a mais da nossa compreensão do mundo, e representa *meta-conhecimento*, por exemplo, alguma noção sobre como e quando o conhecimento deve ser usado. - + *Imagem [da Wikipedia](https://commons.wikimedia.org/w/index.php?curid=37705247), Por Longlivetheux - Trabalho próprio, CC BY-SA 4.0* -Assim, o problema da **representação de conhecimento** é encontrar uma maneira eficaz de representar o conhecimento dentro de um computador na forma de dados, para torná-lo automaticamente utilizável. Isso pode ser visto como um espectro: +Assim, o problema da **representação do conhecimento** é encontrar algum meio eficaz para representar conhecimento dentro de um computador na forma de dados, para torná-lo automaticamente utilizável. Isso pode ser visto como um espectro: -![Espectro de representação de conhecimento](../../../../translated_images/br/knowledge-spectrum.b60df631852c0217.webp) +![Espectro de representação do conhecimento](../../../../../../translated_images/br/knowledge-spectrum.b60df631852c0217.webp) > Imagem por [Dmitry Soshnikov](http://soshnikov.com) -* À esquerda, há tipos muito simples de representações de conhecimento que podem ser usados de forma eficaz por computadores. O mais simples é o algorítmico, quando o conhecimento é representado por um programa de computador. No entanto, essa não é a melhor maneira de representar conhecimento, porque não é flexível. O conhecimento em nossa mente frequentemente não é algorítmico. -* À direita, há representações como texto natural. É a mais poderosa, mas não pode ser usada para raciocínio automático. +* À esquerda, há tipos muito simples de representação do conhecimento que podem ser efetivamente usados por computadores. O mais simples é o algorítmico, quando o conhecimento é representado por um programa de computador. Isso, no entanto, não é a melhor forma de representar o conhecimento, porque não é flexível. O conhecimento dentro da nossa cabeça muitas vezes não é algorítmico. +* À direita, há representações como o texto natural. É a mais poderosa, mas não pode ser usada para raciocínio automático. -> ✅ Pense por um minuto sobre como você representa conhecimento em sua mente e o converte em anotações. Existe um formato específico que funciona bem para você e ajuda na retenção? +> ✅ Pense por um minuto sobre como você representa conhecimento em sua cabeça e o converte em anotações. Existe algum formato particular que funcione bem para você ajudar na retenção? -## Classificando Representações de Conhecimento em Computadores +## Classificando Representações de Conhecimento de Computadores -Podemos classificar diferentes métodos de representação de conhecimento em computadores nas seguintes categorias: +Podemos classificar diferentes métodos de representação do conhecimento em computadores nas seguintes categorias: -* **Representações em rede** são baseadas no fato de que temos uma rede de conceitos inter-relacionados em nossa mente. Podemos tentar reproduzir essas redes como um grafo dentro de um computador - uma chamada **rede semântica**. +* **Representações em rede** baseiam-se no fato de que temos uma rede de conceitos inter-relacionados dentro da nossa cabeça. Podemos tentar reproduzir as mesmas redes como um grafo dentro do computador - uma chamada **rede semântica**. -1. **Triplas Objeto-Atributo-Valor** ou **pares atributo-valor**. Como um grafo pode ser representado dentro de um computador como uma lista de nós e arestas, podemos representar uma rede semântica por uma lista de triplas, contendo objetos, atributos e valores. Por exemplo, construímos as seguintes triplas sobre linguagens de programação: +1. **Triplas Objeto-Atributo-Valor** ou **pares atributo-valor**. Como um grafo pode ser representado em computador como uma lista de nós e arestas, podemos representar uma rede semântica por uma lista de triplas, contendo objetos, atributos e valores. Por exemplo, construímos as seguintes triplas sobre linguagens de programação: Objeto | Atributo | Valor --------|----------|------ +-------|-----------|------ Python | é | Linguagem Não Tipada -Python | inventado-por | Guido van Rossum +Python | inventada-por | Guido van Rossum Python | sintaxe-de-bloco | indentação -Linguagem Não Tipada | não tem | definições de tipo +Linguagem Não Tipada | não tem | definições de tipos -> ✅ Pense em como as triplas podem ser usadas para representar outros tipos de conhecimento. +> ✅ Pense como triplas podem ser usadas para representar outros tipos de conhecimento. -2. **Representações hierárquicas** enfatizam o fato de que frequentemente criamos uma hierarquia de objetos em nossa mente. Por exemplo, sabemos que o canário é um pássaro, e todos os pássaros têm asas. Também temos alguma ideia sobre qual é a cor usual de um canário e qual é sua velocidade de voo. +2. **Representações hierárquicas** enfatizam o fato de que muitas vezes criamos uma hierarquia de objetos em nossa mente. Por exemplo, sabemos que o canário é um pássaro, e todos os pássaros têm asas. Também temos alguma noção de qual é a cor usual de um canário, e qual é sua velocidade de voo. - - **Representação por quadros** é baseada em representar cada objeto ou classe de objetos como um **quadro** que contém **slots**. Os slots têm valores padrão possíveis, restrições de valor ou procedimentos armazenados que podem ser chamados para obter o valor de um slot. Todos os quadros formam uma hierarquia semelhante à hierarquia de objetos em linguagens de programação orientadas a objetos. - - **Cenários** são um tipo especial de quadros que representam situações complexas que podem se desenrolar ao longo do tempo. + - **Representação em frames** baseia-se em representar cada objeto ou classe de objetos como um **frame** que contém **slots**. Slots têm valores padrão possíveis, restrições de valor ou procedimentos armazenados que podem ser chamados para obter o valor de um slot. Todos os frames formam uma hierarquia semelhante a uma hierarquia de objetos em linguagens de programação orientadas a objetos. + - **Cenários** são um tipo especial de frame que representam situações complexas que podem se desenrolar no tempo. **Python** Slot | Valor | Valor padrão | Intervalo | ------|-------|--------------|-----------| +-----|-------|---------------|----------| Nome | Python | | | É-Um | Linguagem Não Tipada | | | Caso de Variável | | CamelCase | | Comprimento do Programa | | | 5-5000 linhas | Sintaxe de Bloco | Indentação | | | -3. **Representações procedurais** são baseadas em representar conhecimento por uma lista de ações que podem ser executadas quando uma certa condição ocorre. - - Regras de produção são declarações do tipo se-então que nos permitem tirar conclusões. Por exemplo, um médico pode ter uma regra dizendo que **SE** um paciente tem febre alta **OU** alto nível de proteína C-reativa no exame de sangue **ENTÃO** ele tem uma inflamação. Uma vez que encontramos uma das condições, podemos concluir sobre a inflamação e, então, usá-la em raciocínios posteriores. +3. **Representações procedurais** baseiam-se em representar o conhecimento por uma lista de ações que podem ser executadas quando uma certa condição ocorre. + - Regras de produção são declarações do tipo se-então que nos permitem tirar conclusões. Por exemplo, um médico pode ter uma regra dizendo que **SE** um paciente tem febre alta **OU** nível alto de proteína C-reativa no exame de sangue **ENTÃO** ele tem uma inflamação. Uma vez que encontramos uma das condições, podemos concluir que há inflamação e usar isso em raciocínios posteriores. - Algoritmos podem ser considerados outra forma de representação procedural, embora quase nunca sejam usados diretamente em sistemas baseados em conhecimento. -4. **Lógica** foi originalmente proposta por Aristóteles como uma forma de representar o conhecimento humano universal. - - Lógica de Predicados como teoria matemática é muito rica para ser computável, portanto, algum subconjunto dela é normalmente usado, como cláusulas de Horn usadas no Prolog. - - Lógica Descritiva é uma família de sistemas lógicos usados para representar e raciocinar sobre hierarquias de objetos em representações de conhecimento distribuído, como a *web semântica*. +4. **Lógica** foi originalmente proposta por Aristóteles como uma forma de representar conhecimento universal humano. + - A Lógica de Predicados, como teoria matemática, é rica demais para ser computável, portanto algum subconjunto dela é normalmente usado, como cláusulas de Horn usadas em Prolog. + - Lógica Descritiva é uma família de sistemas lógicos usados para representar e raciocinar sobre hierarquias de objetos e representações distribuídas de conhecimento, como a *web semântica*. ## Sistemas Especialistas -Um dos primeiros sucessos da IA simbólica foram os chamados **sistemas especialistas** - sistemas computacionais projetados para agir como especialistas em um domínio de problema limitado. Eles eram baseados em uma **base de conhecimento** extraída de um ou mais especialistas humanos e continham um **motor de inferência** que realizava algum raciocínio sobre ela. +Um dos primeiros sucessos da IA simbólica foram os chamados **sistemas especialistas** - sistemas computacionais desenhados para atuar como um especialista em algum domínio de problema limitado. Eles foram baseados em uma **base de conhecimento** extraída de um ou mais especialistas humanos, e continham um **motor de inferência** que realizava algum raciocínio sobre ela. -![Arquitetura Humana](../../../../translated_images/br/arch-human.5d4d35f1bba3ab1c.webp) | ![Sistema Baseado em Conhecimento](../../../../translated_images/br/arch-kbs.3ec5c150b09fa8da.webp) +![Arquitetura Humana](../../../../../../translated_images/br/arch-human.5d4d35f1bba3ab1c.webp) | ![Sistema Baseado em Conhecimento](../../../../../../translated_images/br/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ Estrutura simplificada do sistema neural humano | Arquitetura de um sistema baseado em conhecimento -Os sistemas especialistas são construídos como o sistema de raciocínio humano, que contém **memória de curto prazo** e **memória de longo prazo**. Da mesma forma, nos sistemas baseados em conhecimento distinguimos os seguintes componentes: +Sistemas especialistas são construídos como o sistema de raciocínio humano, que contém **memória de curto prazo** e **memória de longo prazo**. De maneira semelhante, em sistemas baseados em conhecimento distinguimos os seguintes componentes: -* **Memória do problema**: contém o conhecimento sobre o problema que está sendo resolvido no momento, ou seja, a temperatura ou pressão arterial de um paciente, se ele tem inflamação ou não, etc. Esse conhecimento também é chamado de **conhecimento estático**, porque contém um instantâneo do que sabemos atualmente sobre o problema - o chamado *estado do problema*. -* **Base de conhecimento**: representa o conhecimento de longo prazo sobre um domínio de problema. Ele é extraído manualmente de especialistas humanos e não muda de uma consulta para outra. Como permite navegar de um estado do problema para outro, também é chamado de **conhecimento dinâmico**. -* **Motor de inferência**: orquestra todo o processo de busca no espaço de estados do problema, fazendo perguntas ao usuário quando necessário. Também é responsável por encontrar as regras certas para serem aplicadas a cada estado. +* **Memória do problema**: contém o conhecimento sobre o problema que está sendo atualmente resolvido, ou seja, a temperatura ou pressão arterial de um paciente, se ele tem inflamação ou não, etc. Esse conhecimento também é chamado de **conhecimento estático**, porque contém um instantâneo do que sabemos atualmente sobre o problema - o chamado *estado do problema*. +* **Base de conhecimento**: representa o conhecimento de longo prazo sobre um domínio de problema. É extraída manualmente de especialistas humanos, e não muda de consulta para consulta. Como permite navegar de um estado do problema para outro, também é chamada de **conhecimento dinâmico**. +* **Motor de inferência**: orquestra o processo todo de busca no espaço de estados do problema, fazendo perguntas ao usuário quando necessário. Também é responsável por encontrar as regras certas a serem aplicadas para cada estado. Como exemplo, vamos considerar o seguinte sistema especialista para determinar um animal com base em suas características físicas: -![Árvore AND-OR](../../../../translated_images/br/AND-OR-Tree.5592d2c70187f283.webp) +![Árvore AND-OR](../../../../../../translated_images/br/AND-OR-Tree.5592d2c70187f283.webp) > Imagem por [Dmitry Soshnikov](http://soshnikov.com) -Este diagrama é chamado de **árvore AND-OR**, e é uma representação gráfica de um conjunto de regras de produção. Desenhar uma árvore é útil no início da extração de conhecimento do especialista. Para representar o conhecimento dentro do computador, é mais conveniente usar regras: +Esse diagrama é chamado de **árvore AND-OR**, e é uma representação gráfica de um conjunto de regras de produção. Desenhar uma árvore é útil no início da extração de conhecimento do especialista. Para representar o conhecimento dentro do computador, é mais conveniente usar regras: ``` IF the animal eats meat @@ -121,78 +121,78 @@ OR (animal has sharp teeth THEN the animal is a carnivore ``` -Você pode notar que cada condição no lado esquerdo da regra e a ação são essencialmente triplas objeto-atributo-valor (OAV). **Memória de trabalho** contém o conjunto de triplas OAV que correspondem ao problema que está sendo resolvido no momento. Um **motor de regras** procura regras cujas condições são satisfeitas e as aplica, adicionando outra tripla à memória de trabalho. +Você pode notar que cada condição no lado esquerdo da regra e a ação são essencialmente triplas objeto-atributo-valor (OAV). A **memória de trabalho** contém o conjunto de triplas OAV que correspondem ao problema atualmente sendo resolvido. Um **motor de regras** procura as regras cujas condições são satisfeitas e as aplica, adicionando outra tripla à memória de trabalho. -> ✅ Escreva sua própria árvore AND-OR sobre um tópico que você goste! +> ✅ Escreva sua própria árvore AND-OR sobre um tema que você goste! -### Inferência Progressiva vs. Regressiva +### Inferência Direta vs. Inferência Reversa -O processo descrito acima é chamado de **inferência progressiva**. Ele começa com alguns dados iniciais sobre o problema disponíveis na memória de trabalho e, então, executa o seguinte ciclo de raciocínio: +O processo descrito acima é chamado de **inferência direta**. Ele começa com alguns dados iniciais sobre o problema disponíveis na memória de trabalho, e então executa o seguinte ciclo de raciocínio: -1. Se o atributo alvo estiver presente na memória de trabalho - pare e forneça o resultado -2. Procure todas as regras cujas condições estão atualmente satisfeitas - obtenha o **conjunto de conflito** de regras. -3. Realize a **resolução de conflito** - selecione uma regra que será executada nesta etapa. Podem existir diferentes estratégias de resolução de conflito: +1. Se o atributo alvo está presente na memória de trabalho - pare e dê o resultado +2. Procure todas as regras cuja condição está atualmente satisfeita - obtenha o **conjunto de conflito** de regras +3. Realize a **resolução de conflitos** - selecione uma regra que será executada nesta etapa. Podem existir diferentes estratégias de resolução de conflito: - Selecionar a primeira regra aplicável na base de conhecimento - Selecionar uma regra aleatória - - Selecionar uma regra *mais específica*, ou seja, aquela que atende ao maior número de condições no lado esquerdo ("LHS") -4. Aplicar a regra selecionada e inserir um novo pedaço de conhecimento no estado do problema -5. Repetir a partir do passo 1. + - Selecionar uma regra *mais específica*, ou seja, aquela que satisfaz o maior número de condições no "lado esquerdo" (LHS) +4. Aplique a regra selecionada e insira novo conhecimento no estado do problema +5. Repita a partir do passo 1. -No entanto, em alguns casos, podemos querer começar com um conhecimento vazio sobre o problema e fazer perguntas que nos ajudarão a chegar à conclusão. Por exemplo, ao fazer um diagnóstico médico, geralmente não realizamos todos os exames médicos antecipadamente antes de começar a diagnosticar o paciente. Preferimos realizar exames quando uma decisão precisa ser tomada. +Contudo, em alguns casos podemos querer começar com nenhum conhecimento sobre o problema, e fazer perguntas que nos ajudarão a chegar à conclusão. Por exemplo, quando fazemos diagnóstico médico, geralmente não realizamos todos os exames médicos antecipadamente antes de começar a diagnosticar o paciente. Preferimos realizar exames quando uma decisão precisa ser tomada. -Esse processo pode ser modelado usando **inferência regressiva**. Ele é orientado pelo **objetivo** - o valor do atributo que estamos tentando encontrar: +Esse processo pode ser modelado usando **inferência reversa**. Ela é guiada pelo **objetivo** - o valor do atributo que estamos buscando: -1. Selecionar todas as regras que podem nos fornecer o valor de um objetivo (ou seja, com o objetivo no lado direito ("RHS")) - um conjunto de conflito +1. Selecione todas as regras que podem nos dar o valor de um objetivo (ou seja, com o objetivo no Lado Direito ("right-hand-side")) - um conjunto de conflito 1. Se não houver regras para esse atributo, ou houver uma regra dizendo que devemos perguntar o valor ao usuário - pergunte, caso contrário: -1. Use a estratégia de resolução de conflito para selecionar uma regra que usaremos como *hipótese* - tentaremos prová-la -1. Repetir recursivamente o processo para todos os atributos no LHS da regra, tentando prová-los como objetivos -1. Se em algum momento o processo falhar - use outra regra no passo 3. +1. Use a estratégia de resolução de conflitos para selecionar uma regra que usaremos como *hipótese* - tentaremos prová-la +1. Repita recorrentemente o processo para todos os atributos no LHS da regra, tentando prová-los como objetivos +1. Se em algum ponto o processo falhar - use outra regra no passo 3. -> ✅ Em quais situações a inferência progressiva é mais apropriada? E a inferência regressiva? +> ✅ Em quais situações a inferência direta é mais apropriada? E a inferência reversa? ### Implementando Sistemas Especialistas -Os sistemas especialistas podem ser implementados usando diferentes ferramentas: +Sistemas especialistas podem ser implementados usando diferentes ferramentas: -* Programá-los diretamente em alguma linguagem de programação de alto nível. Essa não é a melhor ideia, porque a principal vantagem de um sistema baseado em conhecimento é que o conhecimento é separado da inferência, e potencialmente um especialista no domínio do problema deve ser capaz de escrever regras sem entender os detalhes do processo de inferência. -* Usar um **shell de sistemas especialistas**, ou seja, um sistema projetado especificamente para ser preenchido com conhecimento usando alguma linguagem de representação de conhecimento. +* Programando-os diretamente em alguma linguagem de programação de alto nível. Isso não é a melhor ideia, porque a principal vantagem de um sistema baseado em conhecimento é que o conhecimento está separado da inferência, e potencialmente um especialista no domínio do problema deve ser capaz de escrever regras sem entender os detalhes do processo de inferência +* Usando **shell para sistemas especialistas**, ou seja, um sistema especificamente desenhado para ser povoado com conhecimento usando alguma linguagem de representação de conhecimento. ## ✍️ Exercício: Inferência de Animais -Veja [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) para um exemplo de implementação de sistema especialista com inferência progressiva e regressiva. +Veja [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) para um exemplo de implementação de sistema especialista com inferência direta e reversa. -> **Nota**: Este exemplo é bastante simples e apenas dá uma ideia de como é um sistema especialista. Uma vez que você comece a criar um sistema assim, só perceberá algum comportamento *inteligente* dele quando atingir um certo número de regras, cerca de 200+. Em algum momento, as regras se tornam muito complexas para manter todas em mente, e nesse ponto você pode começar a se perguntar por que o sistema toma certas decisões. No entanto, a característica importante dos sistemas baseados em conhecimento é que você sempre pode *explicar* exatamente como qualquer decisão foi tomada. +> **Nota**: Este exemplo é relativamente simples, e apenas dá a ideia de como um sistema especialista se parece. Quando você começar a criar tal sistema, só notará algum comportamento *inteligente* nele quando alcançar certo número de regras, em torno de 200+. Em algum ponto, as regras se tornam complexas demais para manter todas na mente, e neste ponto você pode começar a se perguntar porque um sistema toma determinadas decisões. No entanto, a característica importante dos sistemas baseados em conhecimento é que você sempre pode *explicar* exatamente como qualquer decisão foi tomada. ## Ontologias e a Web Semântica -No final do século XX, houve uma iniciativa para usar a representação de conhecimento para anotar recursos da Internet, de modo que fosse possível encontrar recursos que correspondam a consultas muito específicas. Esse movimento foi chamado de **Web Semântica**, e baseou-se em vários conceitos: +No final do século 20 houve uma iniciativa para usar representação do conhecimento para anotar recursos da Internet, para que fosse possível encontrar recursos que corresponderiam a consultas muito específicas. Esse movimento foi chamado de **Web Semântica**, e se apoiou em vários conceitos: -- Uma representação de conhecimento especial baseada em **[lógicas descritivas](https://en.wikipedia.org/wiki/Description_logic)** (DL). É semelhante à representação por quadros, porque constrói uma hierarquia de objetos com propriedades, mas tem semântica lógica formal e inferência. Existe uma família inteira de DLs que equilibram entre expressividade e complexidade algorítmica da inferência. -- Representação de conhecimento distribuído, onde todos os conceitos são representados por um identificador global URI, tornando possível criar hierarquias de conhecimento que abrangem a internet. +- Uma representação especial do conhecimento baseada em **[lógicas descritivas](https://en.wikipedia.org/wiki/Description_logic)** (DL). É similar à representação em frames, porque constrói uma hierarquia de objetos com propriedades, mas tem semântica lógica formal e inferência. Existe uma família inteira de DLs que equilibram entre expressividade e complexidade algorítmica da inferência. +- Representação distribuída do conhecimento, onde todos os conceitos são representados por um identificador global URI, tornando possível criar hierarquias de conhecimento que abrangem a internet. - Uma família de linguagens baseadas em XML para descrição de conhecimento: RDF (Resource Description Framework), RDFS (RDF Schema), OWL (Ontology Web Language). -Um conceito central na Web Semântica é o conceito de **Ontologia**. Ele se refere a uma especificação explícita de um domínio de problema usando alguma representação formal de conhecimento. A ontologia mais simples pode ser apenas uma hierarquia de objetos em um domínio de problema, mas ontologias mais complexas incluirão regras que podem ser usadas para inferência. +Um conceito central na Web Semântica é o conceito de **Ontologia**. Refere-se a uma especificação explícita de um domínio do problema usando alguma representação formal de conhecimento. A ontologia mais simples pode ser apenas uma hierarquia de objetos em um domínio do problema, mas ontologias mais complexas incluirão regras que podem ser usadas para inferência. -Na Web Semântica, todas as representações são baseadas em triplas. Cada objeto e cada relação são identificados de forma única por um URI. Por exemplo, se quisermos afirmar o fato de que este Currículo de IA foi desenvolvido por Dmitry Soshnikov em 1º de janeiro de 2022, aqui estão as triplas que podemos usar: +Na web semântica, todas as representações são baseadas em tripletas. Cada objeto e cada relação são identificados unicamente pela URI. Por exemplo, se quisermos afirmar o fato de que este Currículo de IA foi desenvolvido por Dmitry Soshnikov em 1º de janeiro de 2022 - aqui estão as tripletas que podemos usar: - + ``` -http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 13, 2007” +http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 1, 2022” http://github.com/microsoft/ai-for-beginners http://purl.org/dc/elements/1.1/creator http://soshnikov.com ``` -> ✅ Aqui `http://www.example.com/terms/creation-date` e `http://purl.org/dc/elements/1.1/creator` são alguns URIs bem conhecidos e universalmente aceitos para expressar os conceitos de *criador* e *data de criação*. +> ✅ Aqui `http://www.example.com/terms/creation-date` e `http://purl.org/dc/elements/1.1/creator` são algumas URIs bem conhecidas e universalmente aceitas para expressar os conceitos de *criador* e *data de criação*. Em um caso mais complexo, se quisermos definir uma lista de criadores, podemos usar algumas estruturas de dados definidas em RDF. - + > Diagramas acima por [Dmitry Soshnikov](http://soshnikov.com) -O progresso na construção da Web Semântica foi de certa forma desacelerado pelo sucesso dos motores de busca e técnicas de processamento de linguagem natural, que permitem extrair dados estruturados de textos. No entanto, em algumas áreas ainda há esforços significativos para manter ontologias e bases de conhecimento. Alguns projetos que merecem destaque: +O progresso na construção da Web Semântica foi de certa forma desacelerado pelo sucesso dos motores de busca e técnicas de processamento de linguagem natural, que permitem extrair dados estruturados do texto. Contudo, em algumas áreas ainda há esforços significativos para manter ontologias e bases de conhecimento. Alguns projetos que merecem destaque: -* [WikiData](https://wikidata.org/) é uma coleção de bases de conhecimento legíveis por máquinas associadas à Wikipedia. A maior parte dos dados é extraída das *InfoBoxes* da Wikipedia, pedaços de conteúdo estruturado dentro das páginas da Wikipedia. Você pode [consultar](https://query.wikidata.org/) o WikiData em SPARQL, uma linguagem de consulta especial para a Web Semântica. Aqui está uma consulta de exemplo que exibe as cores de olhos mais populares entre os humanos: +* [WikiData](https://wikidata.org/) é uma coleção de bases de conhecimento legíveis por máquina associadas à Wikipedia. A maior parte dos dados é extraída dos *InfoBoxes* da Wikipedia, pedaços de conteúdo estruturado dentro das páginas da Wikipedia. Você pode [consultar](https://query.wikidata.org/) o wikidata em SPARQL, uma linguagem especial de consulta para a Web Semântica. Aqui está uma consulta de exemplo que mostra as cores de olhos mais populares entre humanos: ```sparql #defaultView:BubbleChart @@ -206,47 +206,51 @@ WHERE GROUP BY ?eyeColorLabel ``` -* [DBpedia](https://www.dbpedia.org/) é outro esforço semelhante ao WikiData. +* [DBpedia](https://www.dbpedia.org/) é outro esforço similar ao WikiData. -> ✅ Se você quiser experimentar construir suas próprias ontologias ou abrir ontologias existentes, há um excelente editor visual de ontologias chamado [Protégé](https://protege.stanford.edu/). Baixe-o ou use online. +> ✅ Se você quiser experimentar criar suas próprias ontologias, ou abrir as existentes, existe um ótimo editor visual de ontologias chamado [Protégé](https://protege.stanford.edu/). Faça o download ou use online. - + *Editor Web Protégé aberto com a ontologia da Família Romanov. Captura de tela por Dmitry Soshnikov* -## ✍️ Exercício: Uma Ontologia Familiar +## ✍️ Exercício: Uma Ontologia de Família -Veja [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) para um exemplo de uso de técnicas da Web Semântica para raciocinar sobre relações familiares. Vamos pegar uma árvore genealógica representada no formato comum GEDCOM e uma ontologia de relações familiares e construir um gráfico de todas as relações familiares para um conjunto de indivíduos. +Veja [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) para um exemplo do uso de técnicas da Web Semântica para raciocinar sobre relacionamentos familiares. Iremos pegar uma árvore genealógica representada no formato comum GEDCOM e uma ontologia de relacionamentos familiares e construir um grafo de todos os relacionamentos familiares para um conjunto dado de indivíduos. ## Microsoft Concept Graph -Na maioria dos casos, ontologias são cuidadosamente criadas manualmente. No entanto, também é possível **extrair** ontologias de dados não estruturados, por exemplo, de textos em linguagem natural. +Na maioria dos casos, ontologias são cuidadosamente criadas manualmente. Entretanto, também é possível **extrair** ontologias de dados não estruturados, por exemplo, de textos em linguagem natural. -Uma dessas tentativas foi realizada pela Microsoft Research, resultando no [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). +Uma dessas tentativas foi feita pela Microsoft Research e resultou no [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). -É uma grande coleção de entidades agrupadas usando a relação de herança `is-a`. Permite responder perguntas como "O que é a Microsoft?" - a resposta sendo algo como "uma empresa com probabilidade de 0,87, e uma marca com probabilidade de 0,75". +É uma grande coleção de entidades agrupadas usando relacionamento de herança `é-um`. Isso permite responder perguntas como "O que é a Microsoft?" - a resposta seria algo como "uma empresa com probabilidade 0,87, e uma marca com probabilidade 0,75". -O Graph está disponível como API REST ou como um grande arquivo de texto que lista todos os pares de entidades. +O Grafo está disponível tanto como REST API, quanto como um grande arquivo de texto para download que lista todos os pares de entidades. -## ✍️ Exercício: Um Concept Graph +## ✍️ Exercício: Um Grafo de Conceitos Experimente o notebook [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) para ver como podemos usar o Microsoft Concept Graph para agrupar artigos de notícias em várias categorias. ## Conclusão -Hoje em dia, IA é frequentemente considerada sinônimo de *Machine Learning* ou *Redes Neurais*. No entanto, um ser humano também exibe raciocínio explícito, algo que atualmente não é tratado por redes neurais. Em projetos do mundo real, o raciocínio explícito ainda é usado para realizar tarefas que exigem explicações ou a capacidade de modificar o comportamento do sistema de maneira controlada. +Hoje em dia, IA é frequentemente considerada sinônimo de *Aprendizado de Máquina* ou *Redes Neurais*. Contudo, um ser humano também exibe raciocínio explícito, algo que atualmente não é tratado pelas redes neurais. Em projetos do mundo real, o raciocínio explícito ainda é usado para realizar tarefas que requerem explicações ou a capacidade de modificar o comportamento do sistema de forma controlada. ## 🚀 Desafio -No notebook de Ontologia Familiar associado a esta lição, há uma oportunidade de experimentar outras relações familiares. Tente descobrir novas conexões entre pessoas na árvore genealógica. +No notebook Ontologia de Família associado a esta lição, há a oportunidade de experimentar outras relações familiares. Tente descobrir novas conexões entre pessoas na árvore genealógica. ## [Quiz pós-aula](https://ff-quizzes.netlify.app/en/ai/quiz/4) ## Revisão & Autoestudo -Pesquise na internet para descobrir áreas onde os humanos tentaram quantificar e codificar conhecimento. Dê uma olhada na Taxonomia de Bloom e volte na história para aprender como os humanos tentaram entender o mundo. Explore o trabalho de Linnaeus para criar uma taxonomia de organismos e observe como Dmitri Mendeleev criou uma forma de descrever e agrupar elementos químicos. Que outros exemplos interessantes você consegue encontrar? +Faça uma pesquisa na internet para descobrir áreas onde humanos tentaram quantificar e codificar conhecimento. Veja a Taxonomia de Bloom, e volte na história para aprender como os humanos tentaram entender seu mundo. Explore o trabalho de Lineu para criar uma taxonomia de organismos, e observe como Dmitri Mendeleev criou uma forma para os elementos químicos serem descritos e agrupados. Quais outros exemplos interessantes você pode encontrar? **Tarefa**: [Construa uma Ontologia](assignment.md) --- + +**Aviso Legal**: +Este documento foi traduzido usando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para alcançar a precisão, esteja ciente de que traduções automáticas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte autorizada. Para informações críticas, recomenda-se tradução profissional humana. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações incorretas decorrentes do uso desta tradução. + \ No newline at end of file diff --git a/translations/el/README.md b/translations/el/README.md index 79e783f6..c3546ad2 100644 --- a/translations/el/README.md +++ b/translations/el/README.md @@ -1,8 +1,8 @@ -[Αραβικά](../ar/README.md) | [Βεγγαλικά](../bn/README.md) | [Βουλγαρικά](../bg/README.md) | [Βιρμανικά (Μιανμάρ)](../my/README.md) | [Κινέζικα (Απλοποιημένα)](../zh/README.md) | [Κινέζικα (Παραδοσιακά, Χονγκ Κονγκ)](../hk/README.md) | [Κινέζικα (Παραδοσιακά, Μακάο)](../mo/README.md) | [Κινέζικα (Παραδοσιακά, Ταϊβάν)](../tw/README.md) | [Κροατικά](../hr/README.md) | [Τσέχικα](../cs/README.md) | [Δανέζικα](../da/README.md) | [Ολλανδικά](../nl/README.md) | [Εσθονικά](../et/README.md) | [Φινλανδικά](../fi/README.md) | [Γαλλικά](../fr/README.md) | [Γερμανικά](../de/README.md) | [Ελληνικά](./README.md) | [Εβραϊκά](../he/README.md) | [Χίντι](../hi/README.md) | [Ουγγρικά](../hu/README.md) | [Ινδονησιακά](../id/README.md) | [Ιταλικά](../it/README.md) | [Ιαπωνικά](../ja/README.md) | [Κανάντα](../kn/README.md) | [Κορεατικά](../ko/README.md) | [Λιθουανικά](../lt/README.md) | [Μαλαισιανά](../ms/README.md) | [Μαλαγιαλάμ](../ml/README.md) | [Μαράθι](../mr/README.md) | [Νεπάλι](../ne/README.md) | [Νιγηριανό Πίτζιν](../pcm/README.md) | [Νορβηγικά](../no/README.md) | [Περσικά (Φαρσί)](../fa/README.md) | [Πολωνικά](../pl/README.md) | [Πορτογαλικά (Βραζιλίας)](../br/README.md) | [Πορτογαλικά (Πορτογαλίας)](../pt/README.md) | [Πουντζάμπι (Γκουρμούκι)](../pa/README.md) | [Ρουμανικά](../ro/README.md) | [Ρωσικά](../ru/README.md) | [Σερβικά (Κυριλλικά)](../sr/README.md) | [Σλοβάκικα](../sk/README.md) | [Σλοβενικά](../sl/README.md) | [Ισπανικά](../es/README.md) | [Σουαχίλι](../sw/README.md) | [Σουηδικά](../sv/README.md) | [Ταγκάλογκ (Φιλιππινέζικα)](../tl/README.md) | [Ταμίλ](../ta/README.md) | [Τελούγκου](../te/README.md) | [Ταϊλανδικά](../th/README.md) | [Τουρκικά](../tr/README.md) | [Ουκρανικά](../uk/README.md) | [Ουρντού](../ur/README.md) | [Βιετναμέζικα](../vi/README.md) +[Αραβικά](../ar/README.md) | [Βεγγαλικά](../bn/README.md) | [Βουλγαρικά](../bg/README.md) | [Βιρμανικά (Μιανμάρ)](../my/README.md) | [Κινέζικα (Απλοποιημένα)](../zh/README.md) | [Κινέζικα (Παραδοσιακά, Χονγκ Κονγκ)](../hk/README.md) | [Κινέζικα (Παραδοσιακά, Μακάου)](../mo/README.md) | [Κινέζικα (Παραδοσιακά, Ταϊβάν)](../tw/README.md) | [Κροατικά](../hr/README.md) | [Τσέχικα](../cs/README.md) | [Δανικά](../da/README.md) | [Ολλανδικά](../nl/README.md) | [Εσθονικά](../et/README.md) | [Φινλανδικά](../fi/README.md) | [Γαλλικά](../fr/README.md) | [Γερμανικά](../de/README.md) | [Ελληνικά](./README.md) | [Εβραϊκά](../he/README.md) | [Χίντι](../hi/README.md) | [Ουγγρικά](../hu/README.md) | [Ινδονησιακά](../id/README.md) | [Ιταλικά](../it/README.md) | [Ιαπωνικά](../ja/README.md) | [Κανάντα](../kn/README.md) | [Κορεατικά](../ko/README.md) | [Λιθουανικά](../lt/README.md) | [Μαλαισιανά](../ms/README.md) | [Μαλαγιαλάμ](../ml/README.md) | [Μαραθικά](../mr/README.md) | [Νεπάλι](../ne/README.md) | [Νιγηριανά Πίνγκιν](../pcm/README.md) | [Νορβηγικά](../no/README.md) | [Περσικά (Φαρσί)](../fa/README.md) | [Πολωνικά](../pl/README.md) | [Πορτογαλικά (Βραζιλίας)](../br/README.md) | [Πορτογαλικά (Πορτογαλίας)](../pt/README.md) | [Πουντζαμπικά (Gurmukhi)](../pa/README.md) | [Ρουμανικά](../ro/README.md) | [Ρωσικά](../ru/README.md) | [Σερβικά (Κυριλλικά)](../sr/README.md) | [Σλοβακικά](../sk/README.md) | [Σλοβενικά](../sl/README.md) | [Ισπανικά](../es/README.md) | [Σουαχίλι](../sw/README.md) | [Σουηδικά](../sv/README.md) | [Ταγκάλιγκ (Φιλιππινέζικα)](../tl/README.md) | [Ταμίλ](../ta/README.md) | [Τελούγκου](../te/README.md) | [Ταϊλανδικά](../th/README.md) | [Τουρκικά](../tr/README.md) | [Ουκρανικά](../uk/README.md) | [Ουρντού](../ur/README.md) | [Βιετναμικά](../vi/README.md) > **Προτιμάτε να Κλωνοποιήσετε Τοπικά;** -> Αυτό το αποθετήριο περιλαμβάνει πάνω από 50 μεταφράσεις γλωσσών που αυξάνουν σημαντικά το μέγεθος λήψης. Για να κλωνοποιήσετε χωρίς μεταφράσεις, χρησιμοποιήστε sparse checkout: +> Αυτό το αποθετήριο περιλαμβάνει 50+ μεταφράσεις γλωσσών, οι οποίες αυξάνουν σημαντικά το μέγεθος λήψης. Για κλωνοποίηση χωρίς μεταφράσεις, χρησιμοποιήστε sparse checkout: > ```bash > git clone --filter=blob:none --sparse https://github.com/microsoft/AI-For-Beginners.git > cd AI-For-Beginners @@ -47,102 +47,102 @@ CO_OP_TRANSLATOR_METADATA: > Αυτό σας δίνει όλα όσα χρειάζεστε για να ολοκληρώσετε το μάθημα με πολύ γρηγορότερη λήψη. -**Εάν επιθυμείτε να υποστηριχθούν επιπλέον γλώσσες, αυτές αναγράφονται [εδώ](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** +**Αν επιθυμείτε να υποστηριχθούν επιπλέον γλώσσες μεταφράσεων, αυτές αναγράφονται [εδώ](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** -## Ενταχθείτε στην Κοινότητα +## Εγγραφείτε στην Κοινότητα [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) ## Τι θα μάθετε -**[Μυαλόχάρτης του Μαθήματος](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** +**[Εννοιολογικός Χάρτης του Μαθήματος](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** -Σε αυτό το αναλυτικό πρόγραμμα, θα μάθετε: +Σε αυτό το πρόγραμμα σπουδών, θα μάθετε: -* Διάφορες προσεγγίσεις στην Τεχνητή Νοημοσύνη, συμπεριλαμβανομένης της "παλιάς καλής" συμβολικής προσέγγισης με **Αναπαράσταση Γνώσης** και συλλογιστική ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). -* **Νευρωνικά Δίκτυα** και **Βαθιά Μάθηση**, που βρίσκονται στον πυρήνα της σύγχρονης AI. Θα εικονογραφήσουμε τις έννοιες πίσω από αυτά τα σημαντικά θέματα χρησιμοποιώντας κώδικα σε δύο από τα πιο δημοφιλή πλαίσια - [TensorFlow](http://Tensorflow.org) και [PyTorch](http://pytorch.org). -* **Νευρωνικές Αρχιτεκτονικές** για εργασία με εικόνες και κείμενο. Θα καλύψουμε σύγχρονα μοντέλα αλλά μπορεί να υπάρχει κάποια έλλειψη στην τελευταία τεχνολογία. -* Λιγότερο δημοφιλείς προσεγγίσεις AI, όπως οι **Γενετικοί Αλγόριθμοι** και τα **Πολυ-πρακτορικά Συστήματα**. +* Διαφορετικές προσεγγίσεις στην Τεχνητή Νοημοσύνη, συμπεριλαμβανομένης της "παραδοσιακής" συμβολικής προσέγγισης με **Αναπαράσταση Γνώσης** και λογική ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). +* **Νευρωνικά Δίκτυα** και **Βαθιά Μάθηση**, που βρίσκονται στον πυρήνα της σύγχρονης AI. Θα αναδείξουμε τις έννοιες πίσω από αυτά τα σημαντικά θέματα χρησιμοποιώντας κώδικα σε δύο από τα πιο δημοφιλή πλαίσια - [TensorFlow](http://Tensorflow.org) και [PyTorch](http://pytorch.org). +* **Νευρωνικές Αρχιτεκτονικές** για εργασία με εικόνες και κείμενο. Θα καλύψουμε πρόσφατα μοντέλα, αλλά ίσως λείπει λίγη από την τελευταία λέξη της τεχνολογίας. +* Λιγότερο δημοφιλείς προσεγγίσεις AI, όπως **Γενετικοί Αλγόριθμοι** και **Συστήματα Πολυ-Πρακτόρων**. -Τι δεν θα καλύψουμε σε αυτό το αναλυτικό πρόγραμμα: +Τι δεν θα καλυφθεί σε αυτό το πρόγραμμα σπουδών: > [Βρείτε όλους τους επιπλέον πόρους για αυτό το μάθημα στη συλλογή μας στο Microsoft Learn](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* Επιχειρηματικές περιπτώσεις χρήσης της **AI στις Επιχειρήσεις**. Σκεφτείτε να ακολουθήσετε την εκπαιδευτική διαδρομή [Εισαγωγή στην AI για επιχειρηματικούς χρήστες](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) στο Microsoft Learn, ή την [Σχολή Επιχειρηματικής AI](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), που αναπτύχθηκε σε συνεργασία με το [INSEAD](https://www.insead.edu/). -* **Κλασική Μηχανική Μάθηση**, που έχει καλή περιγραφή στο [Μάθημα Μηχανικής Μάθησης για Αρχάριους](http://github.com/Microsoft/ML-for-Beginners). -* Πρακτικές εφαρμογές AI που έχουν κατασκευαστεί χρησιμοποιώντας **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. Για αυτό, προτείνουμε να ξεκινήσετε με τα μαθήματα του Microsoft Learn για [όραση](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [επεξεργασία φυσικής γλώσσας](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[Γεννητική AI με Azure OpenAI Service](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** και άλλες. -* Συγκεκριμένα ML **Cloud Frameworks**, όπως [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum), [Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum), ή [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Σκεφτείτε να χρησιμοποιήσετε τις εκπαιδευτικές διαδρομές [Δημιουργήστε και λειτουργήστε λύσεις μηχανικής μάθησης με Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) και [Δημιουργήστε και λειτουργήστε λύσεις μηχανικής μάθησης με Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum). -* **Conversational AI** και **Chat Bots**. Υπάρχει ξεχωριστή εκπαιδευτική διαδρομή [Δημιουργήστε λύσεις συνομιλιακής AI](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum), και μπορείτε επίσης να ανατρέξετε σε [αυτό το άρθρο στο blog](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) για περισσότερες λεπτομέρειες. -* **Βαθιά Μαθηματικά** πίσω από τη βαθιά μάθηση. Για αυτό, προτείνουμε το βιβλίο [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) των Ian Goodfellow, Yoshua Bengio και Aaron Courville, που είναι επίσης διαθέσιμο online στο [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). +* Επιχειρηματικά παραδείγματα χρήσης της **AI στις Επιχειρήσεις**. Σκεφτείτε να παρακολουθήσετε τη διαδρομή μάθησης [Εισαγωγή στην AI για επιχειρηματικούς χρήστες](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) στο Microsoft Learn, ή το [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), που αναπτύχθηκε σε συνεργασία με το [INSEAD](https://www.insead.edu/). +* **Κλασική Μηχανική Μάθηση**, που περιγράφεται καλά στο πρόγραμμα σπουδών μας [Machine Learning for Beginners](http://github.com/Microsoft/ML-for-Beginners). +* Πρακτικές εφαρμογές AI που χρησιμοποιούν **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. Για αυτό, προτείνουμε να ξεκινήσετε με τα μονοπάτια Microsoft Learn για [όραση](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [επεξεργασία φυσικής γλώσσας](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[Γεννητική AI με το Azure OpenAI Service](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** και άλλα. +* Συγκεκριμένα πλαίσια ML στο cloud, όπως [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum), [Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum), ή [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Σκεφτείτε να χρησιμοποιήσετε τα μονοπάτια μάθησης [Build and operate machine learning solutions with Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) και [Build and Operate Machine Learning Solutions with Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum). +* **Συνομιλητική AI** και **Chat Bots**. Υπάρχει ξεχωριστό μονοπάτι μάθησης [Create conversational AI solutions](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum), και μπορείτε επίσης να ανατρέξετε σε [αυτό το blog post](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) για περισσότερες λεπτομέρειες. +* **Βαθιά Μαθηματικά** πίσω από τη βαθιά μάθηση. Για αυτό, προτείνουμε το βιβλίο [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) από τους Ian Goodfellow, Yoshua Bengio και Aaron Courville, που είναι διαθέσιμο και online στο [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). -Για μια ομαλή εισαγωγή στα θέματα _AI στο Cloud_ μπορείτε να σκεφτείτε να ακολουθήσετε την εκπαιδευτική διαδρομή [Ξεκινήστε με την τεχνητή νοημοσύνη στο Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum). +Για μια απλή εισαγωγή στα θέματα _AI στο Cloud_ μπορείτε να παρακολουθήσετε το Μονοπάτι Μάθησης [Get started with artificial intelligence on Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum). # Περιεχόμενο | | Σύνδεσμος Μαθήματος | PyTorch/Keras/TensorFlow | Εργαστήριο | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | -| 0 | [Ρύθμιση Μαθήματος](./lessons/0-course-setup/setup.md) | [Ρύθμιση Περιβάλλοντος Ανάπτυξης](./lessons/0-course-setup/how-to-run.md) | | +| 0 | [Ρύθμιση Μαθήματος](./lessons/0-course-setup/setup.md) | [Ρύθμιση του περιβάλλοντος ανάπτυξής σας](./lessons/0-course-setup/how-to-run.md) | | | I | [**Εισαγωγή στην AI**](./lessons/1-Intro/README.md) | | | | 01 | [Εισαγωγή και Ιστορία της AI](./lessons/1-Intro/README.md) | - | - | | II | **Συμβολική AI** | -| 02 | [Αναπαράσταση Γνώσης και Εμπειρικά Συστήματα](./lessons/2-Symbolic/README.md) | [Εμπειρικά Συστήματα](./lessons/2-Symbolic/Animals.ipynb) / [Οντολογία](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Διάγραμμα Εννοιών](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | +| 02 | [Αναπαράσταση Γνώσης και Συστήματα Ειδικών](./lessons/2-Symbolic/README.md) | [Συστήματα Ειδικών](./lessons/2-Symbolic/Animals.ipynb) / [Οντολογία](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Γράφημα Εννοιών](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | | III | [**Εισαγωγή στα Νευρωνικά Δίκτυα**](./lessons/3-NeuralNetworks/README.md) ||| -| 03 | [Perceptron](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [Notebook](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [Lab](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | -| 04 | [Πολυστρωματικός Perceptron και Δημιουργία του δικού μας Framework](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Notebook](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Lab](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | -| 05 | [Εισαγωγή στα Frameworks (PyTorch/TensorFlow) και Υπερεκπαίδευση](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [PyTorch](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [Keras](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | -| IV | [**Επεξεργασία Εικόνας**](./lessons/4-ComputerVision/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [Εξερεύνηση Επεξεργασίας Εικόνας στο Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | -| 06 | [Εισαγωγή στην Επεξεργασία Εικόνας. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Notebook](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Lab](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | -| 07 | [Συνελικτικά Νευρωνικά Δίκτυα](./lessons/4-ComputerVision/07-ConvNets/README.md) & [Αρχιτεκτονικές CNN](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [Lab](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | -| 08 | [Προεκπαιδευμένα Δίκτυα και Μεταφορά Μάθησης](./lessons/4-ComputerVision/08-TransferLearning/README.md) και [Τεχνάσματα Εκπαίδευσης](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | -| 09 | [Αυτοκωδικοποιητές και VAEs](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | +| 03 | [Περσέπτρων](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [Τετράδιο](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [Εργαστήριο](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | +| 04 | [Πολυεπίπεδο Περσέπτρων και Δημιουργία του δικού μας Πλαισίου Εργασίας](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Τετράδιο](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Εργαστήριο](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | +| 05 | [Εισαγωγή σε Πλαίσια Εργασίας (PyTorch/TensorFlow) και Υπερεκπαίδευση](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [PyTorch](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [Keras](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Εργαστήριο](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | +| IV | [**Υπολογιστική Όραση**](./lessons/4-ComputerVision/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [Εξερευνήστε την Υπολογιστική Όραση στο Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | +| 06 | [Εισαγωγή στην Υπολογιστική Όραση. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Τετράδιο](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Εργαστήριο](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | +| 07 | [Συνελικτικά Νευρωνικά Δίκτυα](./lessons/4-ComputerVision/07-ConvNets/README.md) & [Αρχιτεκτονικές CNN](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [Εργαστήριο](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | +| 08 | [Προεκπαιδευμένα Δίκτυα και Μεταφορά Μάθησης](./lessons/4-ComputerVision/08-TransferLearning/README.md) και [Τεχνάσματα Εκπαίδευσης](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Εργαστήριο](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | +| 09 | [Αυτόματοι Κωδικοποιητές και VAEs](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | | 10 | [Γενετικά Ανταγωνιστικά Δίκτυα & Μεταφορά Καλλιτεχνικού Στυλ](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | -| 11 | [Ανίχνευση Αντικειμένων](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [Lab](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | +| 11 | [Ανίχνευση Αντικειμένων](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [Εργαστήριο](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | | 12 | [Σημασιολογική Τμηματοποίηση. U-Net](./lessons/4-ComputerVision/12-Segmentation/README.md) | [PyTorch](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb) | | -| V | [**Επεξεργασία Φυσικής Γλώσσας**](./lessons/5-NLP/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [Εξερεύνηση Επεξεργασίας Φυσικής Γλώσσας στο Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| +| V | [**Επεξεργασία Φυσικής Γλώσσας**](./lessons/5-NLP/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [Εξερευνήστε την Επεξεργασία Φυσικής Γλώσσας στο Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| | 13 | [Αναπαράσταση Κειμένου. Bow/TF-IDF](./lessons/5-NLP/13-TextRep/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb) | | -| 14 | [Σημασιολογικές λέξεις ενσωματώσεις. Word2Vec και GloVe](./lessons/5-NLP/14-Embeddings/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb) | | -| 15 | [Μοντελοποίηση Γλώσσας. Εκπαίδευση των δικών σου ενσωματώσεων](./lessons/5-NLP/15-LanguageModeling/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [Lab](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | +| 14 | [Σημασιολογικές Ενσωματώσεις Λέξεων. Word2Vec και GloVe](./lessons/5-NLP/14-Embeddings/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb) | | +| 15 | [Μοντελοποίηση Γλώσσας. Εκπαίδευση των δικών σου ενσωματώσεων](./lessons/5-NLP/15-LanguageModeling/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [Εργαστήριο](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | | 16 | [Επαναλαμβανόμενα Νευρωνικά Δίκτυα](./lessons/5-NLP/16-RNN/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNTF.ipynb) | | -| 17 | [Γενετικά Επαναλαμβανόμενα Δίκτυα](./lessons/5-NLP/17-GenerativeNetworks/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb) | [Lab](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | -| 18 | [Transformers. BERT.](./lessons/5-NLP/18-Transformers/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb) /[TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersTF.ipynb) | | -| 19 | [Αναγνώριση Ονομαστικών Οντοτήτων](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [Lab](./lessons/5-NLP/19-NER/lab/README.md) | -| 20 | [Μεγάλα Μοντέλα Γλώσσας, Προγραμματισμός Υποδείξεων και Εργασίες με Λίγα Παραδείγματα](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | +| 17 | [Γενετικά Επαναλαμβανόμενα Δίκτυα](./lessons/5-NLP/17-GenerativeNetworks/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb) | [Εργαστήριο](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | +| 18 | [Μετασχηματιστές. BERT.](./lessons/5-NLP/18-Transformers/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb) /[TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersTF.ipynb) | | +| 19 | [Αναγνώριση Οντοτήτων με Όνομα](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [Εργαστήριο](./lessons/5-NLP/19-NER/lab/README.md) | +| 20 | [Μεγάλα Μοντέλα Γλώσσας, Προγραμματισμός Προτροπών και Εργασίες με Λίγα Παραδείγματα](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | | VI | **Άλλες Τεχνικές Τεχνητής Νοημοσύνης** || | -| 21 | [Γενετικοί Αλγόριθμοι](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Notebook](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | -| 22 | [Ενισχυτική Μάθηση Βαθιάς Μάθησης](./lessons/6-Other/22-DeepRL/README.md) | [PyTorch](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[TensorFlow](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [Lab](./lessons/6-Other/22-DeepRL/lab/README.md) | -| 23 | [Συστήματα Πολλαπλών Πρακτόρων](./lessons/6-Other/23-MultiagentSystems/README.md) | | | +| 21 | [Γενετικοί Αλγόριθμοι](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Τετράδιο](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | +| 22 | [Βαθιά Ενισχυτική Μάθηση](./lessons/6-Other/22-DeepRL/README.md) | [PyTorch](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[TensorFlow](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [Εργαστήριο](./lessons/6-Other/22-DeepRL/lab/README.md) | +| 23 | [Συστήματα Πολυ-Πρακτόρων](./lessons/6-Other/23-MultiagentSystems/README.md) | | | | VII | **Ηθική της Τεχνητής Νοημοσύνης** | | | -| 24 | [Ηθική της Τεχνητής Νοημοσύνης και Υπεύθυνη Τεχνητή Νοημοσύνη](./lessons/7-Ethics/README.md) | [Microsoft Learn: Αρχές Υπεύθυνης Τεχνητής Νοημοσύνης](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | +| 24 | [Ηθική της AI και Υπεύθυνη Τεχνητή Νοημοσύνη](./lessons/7-Ethics/README.md) | [Microsoft Learn: Αρχές Υπεύθυνης AI](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | | IX | **Επιπλέον** | | | -| 25 | [Πολυμορφικά Δίκτυα, CLIP και VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Notebook](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | +| 25 | [Πολυμορφικά Δίκτυα, CLIP και VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Τετράδιο](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | -## Κάθε μάθημα περιέχει +## Κάθε μάθημα περιλαμβάνει * Υλικό προανάγνωσης -* Εκτελέσιμα Jupyter Notebooks, τα οποία συχνά είναι συγκεκριμένα για το framework (**PyTorch** ή **TensorFlow**). Το εκτελέσιμο notebook περιέχει επίσης μεγάλο θεωρητικό υλικό, οπότε για να κατανοήσετε το θέμα χρειάζεται να περάσετε τουλάχιστον από μια έκδοση του notebook (είτε PyTorch είτε TensorFlow). -* **Εργαστήρια** διαθέσιμα για ορισμένα θέματα, που σας δίνουν την ευκαιρία να δοκιμάσετε να εφαρμόσετε το υλικό που μάθατε σε ένα συγκεκριμένο πρόβλημα. -* Ορισμένα τμήματα περιέχουν συνδέσμους σε ενότητες του [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) που καλύπτουν συναφή θέματα. +* Εκτελέσιμα Jupyter Notebooks, τα οποία συχνά είναι ειδικά για το πλαίσιο εργασίας (**PyTorch** ή **TensorFlow**). Το εκτελέσιμο τετράδιο περιέχει επίσης πολύ θεωρητικό υλικό, ώστε για να κατανοήσετε το θέμα πρέπει να περάσετε τουλάχιστον από μία έκδοση του τετραδίου (είτε PyTorch είτε TensorFlow). +* **Εργαστήρια** διαθέσιμα για ορισμένα θέματα, που σας δίνουν την ευκαιρία να δοκιμάσετε την εφαρμογή του υλικού που μάθατε σε ένα συγκεκριμένο πρόβλημα. +* Κάποιες ενότητες περιέχουν συνδέσμους σε [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) ενότητες που καλύπτουν συναφή θέματα. ## Ξεκινώντας -### 🎯 Νέος στην Τεχνητή Νοημοσύνη; Ξεκίνα Εδώ! +### 🎯 Νέος στα AI; Ξεκίνα Εδώ! -Αν είστε εντελώς νέος στην Τεχνητή Νοημοσύνη και θέλετε γρήγορα, πρακτικά παραδείγματα, δείτε τα [**Φιλικά Παραδείγματα για Αρχάριους**](./examples/README.md)! Αυτά περιλαμβάνουν: +Αν είστε εντελώς νέος στα AI και θέλετε γρήγορα, πρακτικά παραδείγματα, δείτε τα [**Φιλικά για Αρχάριους Παραδείγματα**](./examples/README.md)! Αυτά περιλαμβάνουν: -- 🌟 **Hello AI World** - Το πρώτο σας πρόγραμμα AI (αναγνώριση προτύπων) -- 🧠 **Απλό Νευρωνικό Δίκτυο** - Δημιουργήστε ένα νευρωνικό δίκτυο από το μηδέν -- 🖼️ **Ταξινομητής Εικόνων** - Ταξινομήστε εικόνες με λεπτομερείς σχολιασμούς -- 💬 **Συναίσθημα Κειμένου** - Ανάλυση θετικού/αρνητικού κειμένου +- 🌟 **Γεια σου Κόσμε AI** - Το πρώτο σου πρόγραμμα AI (αναγνώριση προτύπων) +- 🧠 **Απλό Νευρωνικό Δίκτυο** - Δημιουργία ενός νευρωνικού δικτύου από το μηδέν +- 🖼️ **Ταξινομητής Εικόνων** - Ταξινόμηση εικόνων με λεπτομερή σχόλια +- 💬 **Συναισθηματική Ανάλυση Κειμένου** - Ανάλυση θετικού/αρνητικού κειμένου -Αυτά τα παραδείγματα έχουν σχεδιαστεί για να σας βοηθήσουν να κατανοήσετε τις έννοιες της Τεχνητής Νοημοσύνης πριν βυθιστείτε στο πλήρες πρόγραμμα σπουδών. +Αυτά τα παραδείγματα έχουν σχεδιαστεί για να σας βοηθήσουν να κατανοήσετε τις έννοιες της ΤΝ πριν βουτήξετε στο πλήρες πρόγραμμα σπουδών. ### 📚 Ρύθμιση Πλήρους Προγράμματος Σπουδών -- Έχουμε δημιουργήσει ένα [μάθημα ρύθμισης](./lessons/0-course-setup/setup.md) για να σας βοηθήσουμε με τη ρύθμιση του περιβάλλοντος ανάπτυξής σας. - Για εκπαιδευτές, έχουμε δημιουργήσει και ένα [μάθημα ρύθμισης προγράμματος σπουδών](./lessons/0-course-setup/for-teachers.md) για εσάς επίσης! -- Πώς να [τρέξετε τον κώδικα σε VSCode ή Codepace](./lessons/0-course-setup/how-to-run.md) +- Έχουμε δημιουργήσει ένα [μάθημα ρύθμισης](./lessons/0-course-setup/setup.md) για να σας βοηθήσουμε με τη ρύθμιση του περιβάλλοντος ανάπτυξής σας. - Για εκπαιδευτικούς, έχουμε επίσης δημιουργήσει ένα [μάθημα ρύθμισης προγράμματος σπουδών](./lessons/0-course-setup/for-teachers.md)! +- Πώς να [τρέξετε τον κώδικα σε VSCode ή Codespace](./lessons/0-course-setup/how-to-run.md) Ακολουθήστε αυτά τα βήματα: -Δημιουργήστε Fork του Αποθετηρίου: Πατήστε το κουμπί "Fork" στην πάνω δεξιά γωνία αυτής της σελίδας. +Κλωνοποιήστε το Αποθετήριο: Κάντε κλικ στο κουμπί "Fork" στην πάνω δεξιά γωνία αυτής της σελίδας. Κλωνοποιήστε το Αποθετήριο: `git clone https://github.com/microsoft/AI-For-Beginners.git` @@ -150,23 +150,23 @@ CO_OP_TRANSLATOR_METADATA: ## Γνωρίστε άλλους Μαθητές -Ενταχθείτε στον [επίσημο διακομιστή AI Discord](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) για να γνωρίσετε και να δικτυωθείτε με άλλους μαθητές που παρακολουθούν αυτό το μάθημα και να λάβετε υποστήριξη. +Εγγραφείτε στον [επίσημο Discord server για ΤΝ](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) για να γνωρίσετε και να δικτυωθείτε με άλλους μαθητές που παρακολουθούν αυτό το μάθημα και να λάβετε υποστήριξη. -Εάν έχετε σχόλια προϊόντος ή ερωτήσεις κατά την ανάπτυξη επισκεφτείτε το [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) +Εάν έχετε σχόλια για το προϊόν ή ερωτήσεις κατά την κατασκευή, επισκεφτείτε το [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) -## Κουίζ +## Κουίζ -> **Μια σημείωση για τα κουίζ**: Όλα τα κουίζ βρίσκονται στον φάκελο Quiz-app στο etc\quiz-app, ή [Ηλεκτρονικά Εδώ](https://ff-quizzes.netlify.app/) Είναι συνδεδεμένα από τα μαθήματα, η εφαρμογή κουίζ μπορεί να τρέχει τοπικά ή να αναπτύσσεται στο Azure. Ακολουθήστε τις οδηγίες στον φάκελο `quiz-app`. Βρίσκονται σε διαδικασία σταδιακής τοπικοποίησης. +> **Σημείωση για τα κουίζ**: Όλα τα κουίζ περιέχονται στον φάκελο Quiz-app στο etc\quiz-app, ή [Online Εδώ](https://ff-quizzes.netlify.app/) Συνδέονται από μέσα στα μαθήματα, η εφαρμογή κουίζ μπορεί να τρέξει τοπικά ή να αναπτυχθεί στο Azure· ακολουθήστε τις οδηγίες στον φάκελο `quiz-app`. Σταδιακά το υλικό μεταφράζεται. ## Ζητείται Βοήθεια -Έχετε προτάσεις ή βρήκατε ορθογραφικά ή κώδικα σφάλματα; Δημιουργήστε ένα issue ή ένα pull request. +Έχετε προτάσεις ή βρήκατε ορθογραφικά ή σφάλματα κώδικα; Δημιουργήστε ένα θέμα ή μια αίτηση pull. ## Ειδικές Ευχαριστίες * **✍️ Κύριος Συγγραφέας:** [Dmitry Soshnikov](http://soshnikov.com), PhD -* **🔥 Συντάκτης:** [Jen Looper](https://twitter.com/jenlooper), PhD -* **🎨 Σχεδιαστής Σημειώσεων:** [Tomomi Imura](https://twitter.com/girlie_mac) +* **🔥 Επιμελήτρια:** [Jen Looper](https://twitter.com/jenlooper), PhD +* **🎨 Εικονογράφος Σημειώσεων:** [Tomomi Imura](https://twitter.com/girlie_mac) * **✅ Δημιουργός Κουίζ:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) * **🙏 Κύριοι Συνεισφέροντες:** [Evgenii Pishchik](https://github.com/Pe4enIks) @@ -189,44 +189,44 @@ CO_OP_TRANSLATOR_METADATA: --- -### Σειρά Generative AI -[![Generative AI για Αρχάριους](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst) -[![Generative AI (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst) -[![Generative AI (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst) -[![Generative AI (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst) +### Σειρά Γενετικής ΤΝ +[![Γενετική ΤΝ για Αρχάριους](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst) +[![Γενετική ΤΝ (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst) +[![Γενετική ΤΝ (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst) +[![Γενετική ΤΝ (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst) --- ### Βασική Μάθηση -[![ML για Αρχάριους](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) +[![Μηχανική Μάθηση για Αρχάριους](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) [![Επιστήμη Δεδομένων για Αρχάριους](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst) [![ΤΝ για Αρχάριους](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst) -[![Κυβερνοασφάλεια για Αρχάριους](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung) -[![Web Dev για Αρχάριους](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst) +[![Ασφάλεια Κυβερνοχώρου για Αρχάριους](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung) +[![Ανάπτυξη Ιστού για Αρχάριους](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst) [![IoT για Αρχάριους](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst) -[![XR Development για Αρχάριους](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst) +[![Ανάπτυξη XR για Αρχάριους](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst) --- ### Σειρά Copilot -[![Copilot για AI Προγραμματισμό σε Ζεύγη](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) +[![Copilot για Εξελιγμένο Προγραμματισμό με ΤΝ](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) [![Copilot για C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst) [![Περιπέτεια Copilot](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst) -## Πώς να Λάβετε Βοήθεια +## Λήψη Βοήθειας -Εάν κολλήσετε ή έχετε ερωτήσεις σχετικά με την κατασκευή εφαρμογών AI. Ενταχθείτε σε συνομιλίες με άλλους μαθητές και έμπειρους προγραμματιστές σχετικά με το MCP. Είναι μια υποστηρικτική κοινότητα όπου οι ερωτήσεις είναι ευπρόσδεκτες και η γνώση μοιράζεται ελεύθερα. +Αν κολλήσετε ή έχετε ερωτήσεις σχετικά με την κατασκευή εφαρμογών ΤΝ. Ενταχθείτε σε συζητήσεις με άλλους μαθητές και έμπειρους προγραμματιστές για το MCP. Είναι μια υποστηρικτική κοινότητα όπου οι ερωτήσεις είναι ευπρόσδεκτες και η γνώση μοιράζεται ελεύθερα. [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -Εάν έχετε σχόλια ή λάθη κατά την ανάπτυξη, επισκεφτείτε: +Εάν έχετε σχόλια ή λάθη κατά την κατασκευή, επισκεφτείτε: [![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) --- -**Αποποίηση Ευθυνών**: -Αυτό το έγγραφο έχει μεταφραστεί χρησιμοποιώντας την υπηρεσία μετάφρασης με τεχνητή νοημοσύνη [Co-op Translator](https://github.com/Azure/co-op-translator). Παρόλο που επιδιώκουμε την ακρίβεια, παρακαλούμε να γνωρίζετε ότι οι αυτόματες μεταφράσεις ενδέχεται να περιέχουν σφάλματα ή ανακρίβειες. Το πρωτότυπο έγγραφο στη γλώσσα του θεωρείται η αυθεντική πηγή. Για κρίσιμες πληροφορίες, συνιστάται επαγγελματική μετάφραση από ανθρώπους. Δεν φέρουμε καμία ευθύνη για τυχόν παρεξηγήσεις ή λανθασμένες ερμηνείες που προκύπτουν από τη χρήση αυτής της μετάφρασης. +**Αποποίηση Ευθύνης**: +Αυτό το έγγραφο έχει μεταφραστεί χρησιμοποιώντας την υπηρεσία αυτόματης μετάφρασης AI [Co-op Translator](https://github.com/Azure/co-op-translator). Παρόλο που επιδιώκουμε την ακρίβεια, παρακαλούμε να έχετε υπόψη ότι οι αυτόματες μεταφράσεις ενδέχεται να περιέχουν λάθη ή ανακρίβειες. Το πρωτότυπο έγγραφο στη μητρική του γλώσσα θα πρέπει να θεωρείται η επίσημη πηγή. Για κρίσιμες πληροφορίες, συνιστάται επαγγελματική μετάφραση από ανθρώπους. Δεν φέρουμε ευθύνη για οποιεσδήποτε παρανοήσεις ή λανθασμένες ερμηνείες που προκύπτουν από τη χρήση αυτής της μετάφρασης. \ No newline at end of file diff --git a/translations/el/lessons/0-course-setup/how-to-run.md b/translations/el/lessons/0-course-setup/how-to-run.md index 1369d743..b63378c4 100644 --- a/translations/el/lessons/0-course-setup/how-to-run.md +++ b/translations/el/lessons/0-course-setup/how-to-run.md @@ -1,21 +1,21 @@ # Πώς να Εκτελέσετε τον Κώδικα -Αυτό το πρόγραμμα σπουδών περιέχει πολλά παραδείγματα και εργαστήρια που μπορείτε να εκτελέσετε. Για να το κάνετε αυτό, χρειάζεστε τη δυνατότητα να εκτελείτε κώδικα Python σε Jupyter Notebooks που παρέχονται ως μέρος αυτού του προγράμματος σπουδών. Έχετε διάφορες επιλογές για να εκτελέσετε τον κώδικα: +Αυτή η διδακτέα ύλη περιέχει πολλά εκτελέσιμα παραδείγματα και εργαστήρια που θα θέλατε να τρέξετε. Για να το κάνετε αυτό, χρειάζεστε τη δυνατότητα εκτέλεσης κώδικα Python σε Jupyter Notebooks που παρέχονται ως μέρος αυτής της διδακτέας ύλης. Έχετε αρκετές επιλογές για να τρέξετε τον κώδικα: ## Εκτέλεση τοπικά στον υπολογιστή σας -Για να εκτελέσετε τον κώδικα τοπικά στον υπολογιστή σας, θα χρειαστεί να έχετε εγκατεστημένη κάποια έκδοση της Python. Προσωπικά, προτείνω την εγκατάσταση του **[miniconda](https://conda.io/en/latest/miniconda.html)** - είναι μια ελαφριά εγκατάσταση που υποστηρίζει τον διαχειριστή πακέτων `conda` για διαφορετικά **εικονικά περιβάλλοντα** Python. +Για να εκτελέσετε τον κώδικα τοπικά στον υπολογιστή σας, απαιτείται μια εγκατάσταση Python. Μία πρόταση είναι να εγκαταστήσετε το **[miniconda](https://conda.io/en/latest/miniconda.html)** - είναι μια αρκετά ελαφριά εγκατάσταση που υποστηρίζει το διαχειριστή πακέτων `conda` για διαφορετικά **εικονικά περιβάλλοντα** Python. -Αφού εγκαταστήσετε το miniconda, θα χρειαστεί να κλωνοποιήσετε το αποθετήριο και να δημιουργήσετε ένα εικονικό περιβάλλον για να το χρησιμοποιήσετε σε αυτό το μάθημα: +Μετά την εγκατάσταση του miniconda, κλωνοποιήστε το αποθετήριο και δημιουργήστε ένα εικονικό περιβάλλον που θα χρησιμοποιηθεί για αυτό το μάθημα: ```bash git clone http://github.com/microsoft/ai-for-beginners @@ -24,56 +24,57 @@ conda env create --name ai4beg --file .devcontainer/environment.yml conda activate ai4beg ``` -### Χρήση του Visual Studio Code με την Επέκταση Python +### Χρήση Visual Studio Code με την Επέκταση Python -Ίσως ο καλύτερος τρόπος να χρησιμοποιήσετε το πρόγραμμα σπουδών είναι να το ανοίξετε στο [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) με την [Επέκταση Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste). +Αυτή η διδακτέα ύλη χρησιμοποιείται καλύτερα όταν ανοίγεται στο [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) με την [Επέκταση Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste). -> **Note**: Μόλις κλωνοποιήσετε και ανοίξετε τον φάκελο στο VS Code, θα σας προτείνει αυτόματα να εγκαταστήσετε τις επεκτάσεις Python. Θα χρειαστεί επίσης να εγκαταστήσετε το miniconda όπως περιγράφεται παραπάνω. +> **Σημείωση**: Μόλις κάνετε κλωνοποίηση και ανοίξετε τον φάκελο στο VS Code, θα προτείνει αυτόματα να εγκαταστήσετε τις επεκτάσεις Python. Επίσης, θα πρέπει να εγκαταστήσετε το miniconda όπως περιγράφεται παραπάνω. -> **Note**: Αν το VS Code σας προτείνει να ανοίξετε το αποθετήριο σε container, θα πρέπει να το απορρίψετε για να χρησιμοποιήσετε την τοπική εγκατάσταση της Python. +> **Σημείωση**: Αν το VS Code σας προτείνει να ανοίξετε ξανά το αποθετήριο σε ένα container, θα πρέπει να αρνηθείτε αυτό για να χρησιμοποιήσετε την τοπική εγκατάσταση Python. -### Χρήση του Jupyter στον Περιηγητή +### Χρήση Jupyter στον Browser -Μπορείτε επίσης να χρησιμοποιήσετε το περιβάλλον Jupyter απευθείας από τον περιηγητή στον υπολογιστή σας. Στην πραγματικότητα, τόσο το κλασικό Jupyter όσο και το Jupyter Hub παρέχουν ένα αρκετά βολικό περιβάλλον ανάπτυξης με αυτόματη συμπλήρωση, επισήμανση κώδικα, κ.λπ. +Μπορείτε επίσης να χρησιμοποιήσετε ένα περιβάλλον Jupyter από τον browser στον δικό σας υπολογιστή. Τόσο το κλασικό Jupyter όσο και το JupyterHub παρέχουν ένα βολικό περιβάλλον ανάπτυξης με αυτόματη συμπλήρωση, επισήμανση κώδικα κτλ. Για να ξεκινήσετε το Jupyter τοπικά, πηγαίνετε στον φάκελο του μαθήματος και εκτελέστε: ```bash jupyter notebook ``` -ή +ή ```bash jupyterhub ``` -Στη συνέχεια, μπορείτε να πλοηγηθείτε σε οποιοδήποτε από τα αρχεία `.ipynb`, να τα ανοίξετε και να ξεκινήσετε να εργάζεστε. +Στη συνέχεια μπορείτε να πλοηγηθείτε σε οποιοδήποτε από τα αρχεία `.ipynb`, να τα ανοίξετε και να ξεκινήσετε να εργάζεστε. ### Εκτέλεση σε container -Μια εναλλακτική λύση στην εγκατάσταση της Python είναι να εκτελέσετε τον κώδικα σε container. Δεδομένου ότι το αποθετήριό μας περιέχει έναν ειδικό φάκελο `.devcontainer` που καθοδηγεί πώς να δημιουργήσετε ένα container για αυτό το αποθετήριο, το VS Code θα σας προτείνει να ανοίξετε τον κώδικα σε container. Αυτό θα απαιτήσει την εγκατάσταση του Docker και είναι πιο περίπλοκο, οπότε το προτείνουμε σε πιο έμπειρους χρήστες. +Μια εναλλακτική στην εγκατάσταση Python θα ήταν να τρέξετε τον κώδικα σε container. Επειδή το αποθετήριό μας παρέχει έναν ειδικό φάκελο `.devcontainer` που καθοδηγεί πώς να δημιουργήσετε ένα container για αυτό το αποθετήριο, το VS Code προσφέρει τη δυνατότητα να ξανανοίξετε τον κώδικα σε container. Αυτό θα απαιτήσει εγκατάσταση Docker, και επίσης είναι πιο πολύπλοκο, οπότε το προτείνουμε σε πιο έμπειρους χρήστες. ## Εκτέλεση στο Cloud -Αν δεν θέλετε να εγκαταστήσετε την Python τοπικά και έχετε πρόσβαση σε κάποιους πόρους στο cloud, μια καλή εναλλακτική είναι να εκτελέσετε τον κώδικα στο cloud. Υπάρχουν διάφοροι τρόποι να το κάνετε αυτό: +Αν δεν θέλετε να εγκαταστήσετε Python τοπικά και έχετε πρόσβαση σε πόρους cloud - μια καλή εναλλακτική είναι να τρέξετε τον κώδικα στο cloud. Υπάρχουν πολλοί τρόποι να το κάνετε: -* Χρησιμοποιώντας το **[GitHub Codespaces](https://github.com/features/codespaces)**, που είναι ένα εικονικό περιβάλλον που δημιουργείται για εσάς στο GitHub και είναι προσβάσιμο μέσω του περιηγητή του VS Code. Αν έχετε πρόσβαση στο Codespaces, μπορείτε απλώς να κάνετε κλικ στο κουμπί **Code** στο αποθετήριο, να ξεκινήσετε ένα codespace και να ξεκινήσετε άμεσα. +* Χρησιμοποιώντας το **[GitHub Codespaces](https://github.com/features/codespaces)**, που είναι ένα εικονικό περιβάλλον δημιουργημένο για εσάς στο GitHub, προσβάσιμο μέσω περιβάλλοντος VS Code στον browser. Αν έχετε πρόσβαση στα Codespaces, απλά κάντε κλικ στο κουμπί **Code** στο αποθετήριο, ξεκινήστε ένα codespace και τρέξτε σε χρόνο μηδέν. +* Χρησιμοποιώντας το **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. Το [Binder](https://mybinder.org) προσφέρει δωρεάν πόρους υπολογισμού στο cloud για ανθρώπους σαν εσάς να δοκιμάσουν κώδικα στο GitHub. Υπάρχει ένα κουμπί στην αρχική σελίδα για να ανοίξετε το αποθετήριο στο Binder - αυτό θα σας πάρει γρήγορα στον ιστότοπο του binder, που θα δημιουργήσει το υποκείμενο container και θα ξεκινήσει μια διεπαφή Jupyter web για εσάς χωρίς καθυστερήσεις. -* Χρησιμοποιώντας το **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. Το [Binder](https://mybinder.org) παρέχει δωρεάν υπολογιστικούς πόρους στο cloud για να δοκιμάσετε κώδικα από το GitHub. Υπάρχει ένα κουμπί στην αρχική σελίδα για να ανοίξετε το αποθετήριο στο Binder - αυτό θα σας μεταφέρει γρήγορα στον ιστότοπο του Binder, ο οποίος θα δημιουργήσει το υποκείμενο container και θα ξεκινήσει τη διαδικτυακή διεπαφή Jupyter για εσάς χωρίς προβλήματα. - -> **Note**: Για να αποτραπεί η κακή χρήση, το Binder έχει αποκλείσει την πρόσβαση σε ορισμένους διαδικτυακούς πόρους. Αυτό μπορεί να εμποδίσει τη λειτουργία κάποιου κώδικα που κατεβάζει μοντέλα ή/και σύνολα δεδομένων από το δημόσιο Διαδίκτυο. Ίσως χρειαστεί να βρείτε κάποιες λύσεις. Επίσης, οι υπολογιστικοί πόροι που παρέχει το Binder είναι αρκετά βασικοί, οπότε η εκπαίδευση θα είναι αργή, ειδικά στα πιο σύνθετα μαθήματα. +> **Σημείωση**: Για την αποφυγή καταχρήσεων, το Binder έχει αποκλεισμένη την πρόσβαση σε ορισμένους διαδικτυακούς πόρους. Αυτό μπορεί να εμποδίσει κάποιον κώδικα που χρησιμοποιεί μοντέλα και/ή σύνολα δεδομένων από το δημόσιο Διαδίκτυο να λειτουργήσει. Μπορεί να χρειαστεί να βρείτε ορισμένες λύσεις. Επίσης, οι πόροι υπολογισμού που παρέχει το Binder είναι βασικοί, οπότε η εκπαίδευση μπορεί να είναι αργή, ειδικά σε μεταγενέστερα, πιο σύνθετα μαθήματα. ## Εκτέλεση στο Cloud με GPU -Μερικά από τα πιο προχωρημένα μαθήματα σε αυτό το πρόγραμμα σπουδών θα επωφεληθούν σημαντικά από την υποστήριξη GPU, καθώς διαφορετικά η εκπαίδευση θα είναι εξαιρετικά αργή. Υπάρχουν μερικές επιλογές που μπορείτε να ακολουθήσετε, ειδικά αν έχετε πρόσβαση στο cloud είτε μέσω του [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) είτε μέσω του ιδρύματός σας: +Μερικά από τα μεταγενέστερα μαθήματα αυτής της ύλης θα ωφεληθούν σημαντικά από την υποστήριξη GPU. Η εκπαίδευση μοντέλων, για παράδειγμα, μπορεί να είναι πολύ αργή διαφορετικά. Υπάρχουν μερικές επιλογές που μπορείτε να ακολουθήσετε, ειδικά αν έχετε πρόσβαση στο cloud είτε μέσω του [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste), είτε μέσω του ιδρύματός σας: -* Δημιουργήστε μια [Εικονική Μηχανή Επιστήμης Δεδομένων](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) και συνδεθείτε σε αυτή μέσω Jupyter. Στη συνέχεια, μπορείτε να κλωνοποιήσετε το αποθετήριο απευθείας στη μηχανή και να ξεκινήσετε τη μάθηση. Οι εικονικές μηχανές της σειράς NC υποστηρίζουν GPU. +* Δημιουργήστε [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) και συνδεθείτε σε αυτήν μέσω Jupyter. Μπορείτε τότε να κλωνοποιήσετε το αποθετήριο απευθείας στη μηχανή και να ξεκινήσετε να μαθαίνετε. Οι NC-series VM υποστηρίζουν GPU. -> **Note**: Ορισμένες συνδρομές, συμπεριλαμβανομένου του Azure for Students, δεν παρέχουν υποστήριξη GPU από προεπιλογή. Ίσως χρειαστεί να ζητήσετε επιπλέον πυρήνες GPU μέσω αιτήματος τεχνικής υποστήριξης. +> **Σημείωση**: Ορισμένες συνδρομές, συμπεριλαμβανομένου του Azure for Students, δεν παρέχουν υποστήριξη GPU απευθείας. Μπορεί να χρειαστεί να ζητήσετε πρόσθετους πυρήνες GPU με αίτημα τεχνικής υποστήριξης. -* Δημιουργήστε ένα [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) και χρησιμοποιήστε τη δυνατότητα Notebook εκεί. [Αυτό το βίντεο](https://azure-for-academics.github.io/quickstart/azureml-papers/) δείχνει πώς να κλωνοποιήσετε ένα αποθετήριο σε Azure ML notebook και να ξεκινήσετε να το χρησιμοποιείτε. +* Δημιουργήστε [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) και χρησιμοποιήστε εκεί τη λειτουργία Notebook. [Αυτό το βίντεο](https://azure-for-academics.github.io/quickstart/azureml-papers/) δείχνει πώς να κλωνοποιήσετε ένα αποθετήριο σε notebook του Azure ML και να το αρχίσετε να το χρησιμοποιείτε. -Μπορείτε επίσης να χρησιμοποιήσετε το Google Colab, το οποίο παρέχει κάποια δωρεάν υποστήριξη GPU, και να ανεβάσετε τα Jupyter Notebooks εκεί για να τα εκτελέσετε ένα προς ένα. +Μπορείτε επίσης να χρησιμοποιήσετε το Google Colab, που προσφέρει κάποια δωρεάν υποστήριξη GPU, και να ανεβάσετε εκεί τα Jupyter Notebooks για να τα εκτελέσετε ένα-ένα. --- -**Αποποίηση ευθύνης**: -Αυτό το έγγραφο έχει μεταφραστεί χρησιμοποιώντας την υπηρεσία αυτόματης μετάφρασης AI [Co-op Translator](https://github.com/Azure/co-op-translator). Παρόλο που καταβάλλουμε προσπάθειες για ακρίβεια, παρακαλούμε να γνωρίζετε ότι οι αυτόματες μεταφράσεις ενδέχεται να περιέχουν σφάλματα ή ανακρίβειες. Το πρωτότυπο έγγραφο στη μητρική του γλώσσα θα πρέπει να θεωρείται η αυθεντική πηγή. Για κρίσιμες πληροφορίες, συνιστάται επαγγελματική ανθρώπινη μετάφραση. Δεν φέρουμε ευθύνη για τυχόν παρεξηγήσεις ή εσφαλμένες ερμηνείες που προκύπτουν από τη χρήση αυτής της μετάφρασης. \ No newline at end of file + +**Αποποίηση ευθυνών**: +Αυτό το έγγραφο έχει μεταφραστεί χρησιμοποιώντας την υπηρεσία μετάφρασης με τεχνητή νοημοσύνη [Co-op Translator](https://github.com/Azure/co-op-translator). Ενώ προσπαθούμε για ακρίβεια, παρακαλούμε σημειώστε ότι οι αυτοματοποιημένες μεταφράσεις ενδέχεται να περιέχουν λάθη ή ανακρίβειες. Το πρωτότυπο έγγραφο στη γλώσσα προέλευσής του πρέπει να θεωρείται η αυθεντική πηγή. Για κρίσιμες πληροφορίες, προτείνεται η επαγγελματική ανθρώπινη μετάφραση. Δεν φέρουμε ευθύνη για τυχόν παρανοήσεις ή λανθασμένες ερμηνείες που προκύπτουν από τη χρήση αυτής της μετάφρασης. + \ No newline at end of file diff --git a/translations/el/lessons/2-Symbolic/Animals.ipynb b/translations/el/lessons/2-Symbolic/Animals.ipynb index a2bb6876..7f79fd04 100644 --- a/translations/el/lessons/2-Symbolic/Animals.ipynb +++ b/translations/el/lessons/2-Symbolic/Animals.ipynb @@ -10,21 +10,21 @@ "\n", "Ένα παράδειγμα από το [AI for Beginners Curriculum](http://github.com/microsoft/ai-for-beginners).\n", "\n", - "Σε αυτό το παράδειγμα, θα υλοποιήσουμε ένα απλό σύστημα βασισμένο στη γνώση για να προσδιορίσουμε ένα ζώο με βάση ορισμένα φυσικά χαρακτηριστικά. Το σύστημα μπορεί να αναπαρασταθεί από το παρακάτω δέντρο AND-OR (αυτό είναι ένα μέρος του συνολικού δέντρου, μπορούμε εύκολα να προσθέσουμε περισσότερους κανόνες):\n", + "Σε αυτό το δείγμα, θα υλοποιήσουμε ένα απλό σύστημα βασισμένο σε γνώση για να προσδιορίσουμε ένα ζώο βάσει ορισμένων φυσικών χαρακτηριστικών. Το σύστημα μπορεί να αναπαρασταθεί από το ακόλουθο δέντρο AND-OR (αυτή είναι ένα μέρος του συνολικού δέντρου, μπορούμε εύκολα να προσθέσουμε κι άλλους κανόνες):\n", "\n", - "![](../../../../translated_images/el/AND-OR-Tree.5592d2c70187f283.webp)\n" + "![](../../../../../../translated_images/el/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Το δικό μας κέλυφος συστημάτων ειδικών γνώσεων με οπισθοδρομική συμπερασματολογία\n", + "## Το δικό μας κέλυφος συστημάτων ειδικών με οπισθοδρομική συμπερασματολογία\n", "\n", - "Ας προσπαθήσουμε να ορίσουμε μια απλή γλώσσα για την αναπαράσταση γνώσης βασισμένη σε κανόνες παραγωγής. Θα χρησιμοποιήσουμε κλάσεις της Python ως λέξεις-κλειδιά για τον ορισμό κανόνων. Υπάρχουν ουσιαστικά 3 τύποι κλάσεων:\n", - "* `Ask` αντιπροσωπεύει μια ερώτηση που πρέπει να γίνει στον χρήστη. Περιέχει το σύνολο των πιθανών απαντήσεων.\n", - "* `If` αντιπροσωπεύει έναν κανόνα και είναι απλώς συντακτική διευκόλυνση για την αποθήκευση του περιεχομένου του κανόνα.\n", - "* `AND`/`OR` είναι κλάσεις που αντιπροσωπεύουν τα κλαδιά AND/OR του δέντρου. Απλώς αποθηκεύουν τη λίστα των επιχειρημάτων μέσα. Για την απλοποίηση του κώδικα, όλη η λειτουργικότητα ορίζεται στην γονική κλάση `Content`.\n" + "Ας προσπαθήσουμε να ορίσουμε μια απλή γλώσσα για την αναπαράσταση γνώσης βασισμένη σε κανόνες παραγωγής. Θα χρησιμοποιήσουμε τις κλάσεις Python ως λέξεις-κλειδιά για τον ορισμό κανόνων. Θα υπάρχουν ουσιαστικά 3 τύποι κλάσεων:\n", + "* Η `Ask` αναπαριστά μια ερώτηση που πρέπει να τεθεί στον χρήστη. Περιέχει το σύνολο των πιθανών απαντήσεων.\n", + "* Η `If` αναπαριστά έναν κανόνα και είναι απλώς μια γλωσσική συντόμευση για την αποθήκευση του περιεχομένου του κανόνα.\n", + "* Οι `AND`/`OR` είναι κλάσεις που αναπαριστούν τα υποδέντρα AND/OR. Απλώς αποθηκεύουν τη λίστα των ορισμάτων στο εσωτερικό τους. Για να απλοποιηθεί ο κώδικας, όλες οι λειτουργίες ορίζονται στην κλάση γονέα `Content`.\n" ] }, { @@ -66,7 +66,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Στο σύστημά μας, η ενεργή μνήμη θα περιέχει τη λίστα των **γεγονότων** ως **ζεύγη χαρακτηριστικών-τιμών**. Η βάση γνώσεων μπορεί να οριστεί ως ένα μεγάλο λεξικό που αντιστοιχεί ενέργειες (νέα γεγονότα που πρέπει να εισαχθούν στην ενεργή μνήμη) σε συνθήκες, εκφρασμένες ως εκφράσεις AND-OR. Επίσης, ορισμένα γεγονότα μπορούν να `ζητηθούν`.\n" + "Στο σύστημά μας, η εργαζόμενη μνήμη θα περιέχει τη λίστα των **γεγονότων** ως **ζεύγη ιδιοτήτων-τιμών**. Η βάση γνώσεων μπορεί να οριστεί ως ένα μεγάλο λεξικό που αντιστοιχίζει ενέργειες (νέα γεγονότα που πρέπει να εισαχθούν στην εργαζόμενη μνήμη) σε συνθήκες, εκφρασμένες ως εκφράσεις ΚΑΙ-Ή. Επίσης, ορισμένα γεγονότα μπορούν να `Ask`-θούν.\n" ] }, { @@ -99,13 +99,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Για να πραγματοποιήσουμε την αντίστροφη συμπερασματολογία, θα ορίσουμε την κλάση `Knowledgebase`. Αυτή θα περιλαμβάνει:\n", - "* Λειτουργική `μνήμη` - ένα λεξικό που αντιστοιχεί χαρακτηριστικά σε τιμές\n", - "* `Κανόνες` της βάσης γνώσης στη μορφή που ορίστηκε παραπάνω\n", + "Για να πραγματοποιήσουμε την οπισθογόρηση, θα ορίσουμε την κλάση `Knowledgebase`. Θα περιέχει:\n", + "* Εργασιακή `μνήμη` - ένα λεξικό που αντιστοιχεί χαρακτηριστικά σε τιμές\n", + "* Κανόνες της Βάσης Γνώσης `rules` στη μορφή που ορίζεται παραπάνω\n", "\n", - "Δύο βασικές μέθοδοι είναι:\n", - "* `get` για την απόκτηση της τιμής ενός χαρακτηριστικού, πραγματοποιώντας συμπερασματολογία αν είναι απαραίτητο. Για παράδειγμα, `get('color')` θα πάρει την τιμή μιας θέσης χρώματος (θα ρωτήσει αν είναι απαραίτητο και θα αποθηκεύσει την τιμή για μελλοντική χρήση στη λειτουργική μνήμη). Αν ρωτήσουμε `get('color:blue')`, θα ζητήσει ένα χρώμα και στη συνέχεια θα επιστρέψει τιμή `y`/`n` ανάλογα με το χρώμα.\n", - "* `eval` πραγματοποιεί την πραγματική συμπερασματολογία, δηλαδή διατρέχει το δέντρο AND/OR, αξιολογεί υπο-στόχους, κλπ.\n" + "Δύο κύριες μέθοδοι είναι:\n", + "* `get` για να λάβουμε την τιμή ενός χαρακτηριστικού, πραγματοποιώντας οινοποίηση αν χρειάζεται. Για παράδειγμα, το `get('color')` θα πάρει την τιμή ενός πεδίου χρώματος (θα ρωτήσει αν χρειάζεται και θα αποθηκεύσει την τιμή για μελλοντική χρήση στην εργασιακή μνήμη). Αν ζητήσουμε `get('color:blue')`, θα ζητήσει χρώμα και μετά θα επιστρέψει τιμή `y`/`n` ανάλογα με το χρώμα.\n", + "* `eval` εκτελεί την πραγματική λογική, δηλαδή διασχίζει το δέντρο AND/OR, αξιολογεί υπο-στόχους κλπ.\n" ] }, { @@ -172,7 +172,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Τώρα ας ορίσουμε τη βάση γνώσεων για τα ζώα και να πραγματοποιήσουμε τη συμβουλευτική. Σημειώστε ότι αυτή η κλήση θα σας κάνει ερωτήσεις. Μπορείτε να απαντήσετε πληκτρολογώντας `y`/`n` για ερωτήσεις ναι-όχι, ή καθορίζοντας αριθμό (0..N) για ερωτήσεις με περισσότερες επιλογές απαντήσεων.\n" + "Τώρα ας ορίσουμε τη βάση γνώσεων για τα ζώα μας και να πραγματοποιήσουμε τη διαβούλευση. Σημειώστε ότι αυτή η κλήση θα σας κάνει ερωτήσεις. Μπορείτε να απαντήσετε πληκτρολογώντας `y`/`n` για ερωτήσεις ναι-όχι, ή καθορίζοντας έναν αριθμό (0..N) για ερωτήσεις με μεγαλύτερες απαντήσεις πολλαπλής επιλογής.\n" ] }, { @@ -229,11 +229,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Χρήση του PyKnow για Προωθητική Συμπερασματολογία\n", + "## Χρήση του Experta για Προώθηση Συμπερασμάτων\n", "\n", - "Στο επόμενο παράδειγμα, θα προσπαθήσουμε να υλοποιήσουμε προωθητική συμπερασματολογία χρησιμοποιώντας μία από τις βιβλιοθήκες για αναπαράσταση γνώσης, [PyKnow](https://github.com/buguroo/pyknow/). Το **PyKnow** είναι μια βιβλιοθήκη για τη δημιουργία συστημάτων προωθητικής συμπερασματολογίας σε Python, η οποία έχει σχεδιαστεί ώστε να μοιάζει με το κλασικό παλιό σύστημα [CLIPS](http://www.clipsrules.net/index.html).\n", + "Στο επόμενο παράδειγμα, θα προσπαθήσουμε να υλοποιήσουμε προώθηση συμπερασμάτων χρησιμοποιώντας μία από τις βιβλιοθήκες για αναπαράσταση γνώσης, το [Experta](https://github.com/nilp0inter/experta). **Το Experta** είναι μία βιβλιοθήκη για τη δημιουργία συστημάτων προώθησης συμπερασμάτων σε Python, η οποία έχει σχεδιαστεί ώστε να είναι παρόμοια με το κλασικό παλιό σύστημα [CLIPS](http://www.clipsrules.net/index.html).\n", "\n", - "Θα μπορούσαμε επίσης να υλοποιήσουμε την προωθητική αλυσίδωση μόνοι μας χωρίς ιδιαίτερα προβλήματα, αλλά οι απλοϊκές υλοποιήσεις συνήθως δεν είναι πολύ αποδοτικές. Για πιο αποτελεσματική αντιστοίχιση κανόνων χρησιμοποιείται ένας ειδικός αλγόριθμος, [Rete](https://en.wikipedia.org/wiki/Rete_algorithm).\n" + "Θα μπορούσαμε επίσης να υλοποιήσουμε την προώθηση συμπερασμάτων μόνοι μας χωρίς πολλά προβλήματα, αλλά οι απλές υλοποιήσεις συνήθως δεν είναι πολύ αποδοτικές. Για πιο αποτελεσματική αντιστοίχιση κανόνων χρησιμοποιείται ένας ειδικός αλγόριθμος [Rete](https://en.wikipedia.org/wiki/Rete_algorithm).\n" ] }, { @@ -247,32 +247,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "Collecting git+https://github.com/buguroo/pyknow/\n", - " Cloning https://github.com/buguroo/pyknow/ to /tmp/pip-req-build-3cqeulyl\n", - " Running command git clone --filter=blob:none --quiet https://github.com/buguroo/pyknow/ /tmp/pip-req-build-3cqeulyl\n", - " Resolved https://github.com/buguroo/pyknow/ to commit 48818336f2e9a126f1964f2d8dc22d37ff800fe8\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting frozendict==1.2\n", - " Using cached frozendict-1.2.tar.gz (2.6 kB)\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting schema==0.6.7\n", - " Using cached schema-0.6.7-py2.py3-none-any.whl (14 kB)\n", - "Building wheels for collected packages: pyknow, frozendict\n", - " Building wheel for pyknow (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for pyknow: filename=pyknow-1.7.0-py3-none-any.whl size=34228 sha256=b7de5b09292c4007667c72f69b98d5a1b5f7324ff15f9dd8e077c3d5f7aade42\n", - " Stored in directory: /tmp/pip-ephem-wheel-cache-k7jpave7/wheels/81/1a/d3/f6c15dbe1955598a37755215f2a10449e7418500d7bd4b9508\n", - " Building wheel for frozendict (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for frozendict: filename=frozendict-1.2-py3-none-any.whl size=3148 sha256=2863d55c240d2409cddf05ccfe600591f8478681549fc97555c47c90dc6bb160\n", - " Stored in directory: /home/rg/.cache/pip/wheels/49/ac/f8/cb8120244e710bdb479c86198b03c7b08c3c2d3d2bf448fd6e\n", - "Successfully built pyknow frozendict\n", - "Installing collected packages: schema, frozendict, pyknow\n", - "Successfully installed frozendict-1.2 pyknow-1.7.0 schema-0.6.7\n" + "Collecting git+https://github.com/nilp0inter/experta\n", + " Cloning https://github.com/nilp0inter/experta to /tmp/pip-req-build-7qurtwk3\n", + " Running command git clone --filter=blob:none --quiet https://github.com/nilp0inter/experta /tmp/pip-req-build-7qurtwk3\n", + " Resolved https://github.com/nilp0inter/experta to commit c6d5834b123861f5ae09e7d07027dc98bec58741\n", + " Installing build dependencies ... \u001b[?25ldone\n", + "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", + "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25hRequirement already satisfied: frozendict~=2.4.6 in /opt/conda/envs/ai4beg/lib/python3.12/site-packages (from experta==1.9.5.dev1) (2.4.7)\n", + "Collecting schema~=0.6.7 (from experta==1.9.5.dev1)\n", + " Downloading schema-0.6.8-py2.py3-none-any.whl.metadata (14 kB)\n", + "Downloading schema-0.6.8-py2.py3-none-any.whl (14 kB)\n", + "Building wheels for collected packages: experta\n", + " Building wheel for experta (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25h Created wheel for experta: filename=experta-1.9.5.dev1-py3-none-any.whl size=34804 sha256=888c459512a5e713f4b674caa9a0f96cfdf07ec0d6eb56cc318ce0653d218014\n", + " Stored in directory: /tmp/pip-ephem-wheel-cache-1eeii9zy/wheels/3d/e8/bb/22d7956359603fa8dd679aa09f5b8efb3f29991c3986fdc787\n", + "Successfully built experta\n", + "Installing collected packages: schema, experta\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2/2\u001b[0m [experta]\n", + "\u001b[1A\u001b[2KSuccessfully installed experta-1.9.5.dev1 schema-0.6.8\n" ] } ], "source": [ "import sys\n", - "!{sys.executable} -m pip install git+https://github.com/buguroo/pyknow/" + "!{sys.executable} -m pip install git+https://github.com/nilp0inter/experta" ] }, { @@ -283,15 +282,15 @@ }, "outputs": [], "source": [ - "from pyknow import *\n", - "#import pyknow" + "from experta import *\n", + "#import experta" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Θα ορίσουμε το σύστημά μας ως μια κλάση που υποκλάση την `KnowledgeEngine`. Κάθε κανόνας ορίζεται από μια ξεχωριστή συνάρτηση με την αναnotation `@Rule`, η οποία καθορίζει πότε πρέπει να ενεργοποιηθεί ο κανόνας. Μέσα στον κανόνα, μπορούμε να προσθέσουμε νέα δεδομένα χρησιμοποιώντας τη συνάρτηση `declare`, και η προσθήκη αυτών των δεδομένων θα έχει ως αποτέλεσμα να κληθούν κάποιοι επιπλέον κανόνες από τη μηχανή προώθησης συμπερασμάτων.\n" + "Θα ορίσουμε το σύστημά μας ως μια κλάση που υποκλάσης την `KnowledgeEngine`. Κάθε κανόνας ορίζεται από μια ξεχωριστή συνάρτηση με τη σημείωση `@Rule`, η οποία καθορίζει πότε πρέπει να ενεργοποιηθεί ο κανόνας. Μέσα στον κανόνα, μπορούμε να προσθέσουμε νέα γεγονότα χρησιμοποιώντας τη συνάρτηση `declare`, και η προσθήκη αυτών των γεγονότων θα έχει ως αποτέλεσμα ορισμένοι ακόμα κανόνες να καλούνται από τη μηχανή εμπρόθετης συμπερασματολογίας.\n" ] }, { @@ -378,7 +377,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Αφού ορίσουμε μια βάση γνώσεων, γεμίζουμε τη λειτουργική μας μνήμη με κάποια αρχικά γεγονότα και στη συνέχεια καλούμε τη μέθοδο `run()` για να εκτελέσουμε την εξαγωγή συμπερασμάτων. Μπορείτε να δείτε ως αποτέλεσμα ότι νέα συμπεράσματα προστίθενται στη λειτουργική μνήμη, συμπεριλαμβανομένου του τελικού γεγονότος σχετικά με το ζώο (αν έχουμε ορίσει σωστά όλα τα αρχικά γεγονότα).\n" + "Αφού ορίσουμε μια βάση γνώσης, γεμίζουμε τη μνήμη εργασίας μας με μερικά αρχικά δεδομένα και στη συνέχεια καλούμε τη μέθοδο `run()` για να εκτελέσουμε την επαγωγή. Μπορείτε να δείτε ως αποτέλεσμα ότι νέα συμπερασμένα δεδομένα προστίθενται στη μνήμη εργασίας, συμπεριλαμβανομένου του τελικού δεδομένου για το ζώο (αν έχουμε ορίσει σωστά όλα τα αρχικά δεδομένα).\n" ] }, { @@ -440,7 +439,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n---\n\n**Αποποίηση ευθύνης**: \nΑυτό το έγγραφο έχει μεταφραστεί χρησιμοποιώντας την υπηρεσία αυτόματης μετάφρασης AI [Co-op Translator](https://github.com/Azure/co-op-translator). Παρόλο που καταβάλλουμε κάθε προσπάθεια για ακρίβεια, παρακαλούμε να έχετε υπόψη ότι οι αυτόματες μεταφράσεις ενδέχεται να περιέχουν σφάλματα ή ανακρίβειες. Το πρωτότυπο έγγραφο στη μητρική του γλώσσα θα πρέπει να θεωρείται η αυθεντική πηγή. Για κρίσιμες πληροφορίες, συνιστάται επαγγελματική ανθρώπινη μετάφραση. Δεν φέρουμε ευθύνη για τυχόν παρεξηγήσεις ή εσφαλμένες ερμηνείες που προκύπτουν από τη χρήση αυτής της μετάφρασης.\n" + "---\n\n\n**Αποποίηση ευθυνών**: \nΑυτό το έγγραφο έχει μεταφραστεί χρησιμοποιώντας την υπηρεσία μετάφρασης AI [Co-op Translator](https://github.com/Azure/co-op-translator). Παρότι επιδιώκουμε ακρίβεια, παρακαλούμε να γνωρίζετε ότι οι αυτοματοποιημένες μεταφράσεις ενδέχεται να περιέχουν λάθη ή ανακρίβειες. Το πρωτότυπο έγγραφο στη μητρική του γλώσσα πρέπει να θεωρείται ως η έγκυρη πηγή. Για κρίσιμες πληροφορίες, συνιστάται επαγγελματική μετάφραση από άνθρωπο. Δεν φέρουμε ευθύνη για τυχόν παρεξηγήσεις ή λανθασμένες ερμηνείες που προκύπτουν από τη χρήση αυτής της μετάφρασης.\n\n" ] } ], @@ -467,8 +466,8 @@ "version": "3.11.2" }, "coopTranslator": { - "original_hash": "ab2bd97b0453415b89a469284609a8ce", - "translation_date": "2025-08-29T10:15:28+00:00", + "original_hash": "8ef43db4b9182239fd150a76bd494fdb", + "translation_date": "2026-01-15T14:05:47+00:00", "source_file": "lessons/2-Symbolic/Animals.ipynb", "language_code": "el" } diff --git a/translations/el/lessons/2-Symbolic/README.md b/translations/el/lessons/2-Symbolic/README.md index 079e5bf0..cc4fb55e 100644 --- a/translations/el/lessons/2-Symbolic/README.md +++ b/translations/el/lessons/2-Symbolic/README.md @@ -1,112 +1,112 @@ -# Αναπαράσταση Γνώσης και Συστήματα Ειδικών +# Αναπαράσταση Γνώσης και Ειδικά Συστήματα -![Περίληψη περιεχομένου Συμβολικής Τεχνητής Νοημοσύνης](../../../../translated_images/el/ai-symbolic.715a30cb610411a6.webp) +![Περίληψη περιεχομένου Συμβολικής Τεχνητής Νοημοσύνης](../../../../../../translated_images/el/ai-symbolic.715a30cb610411a6.webp) -> Σχεδιαστικό σημείωμα από [Tomomi Imura](https://twitter.com/girlie_mac) +> Σκίτσο από [Tomomi Imura](https://twitter.com/girlie_mac) -Η αναζήτηση της τεχνητής νοημοσύνης βασίζεται στην αναζήτηση της γνώσης, ώστε να κατανοηθεί ο κόσμος με τρόπο παρόμοιο με αυτόν των ανθρώπων. Αλλά πώς μπορεί να επιτευχθεί αυτό; +Η αναζήτηση της τεχνητής νοημοσύνης βασίζεται στην αναζήτηση γνώσης, για να κατανοήσει τον κόσμο παρόμοια με τον τρόπο που το κάνουν οι άνθρωποι. Αλλά πώς μπορεί κάποιος να το κάνει αυτό; -## [Κουίζ πριν το μάθημα](https://ff-quizzes.netlify.app/en/ai/quiz/3) +## [Προ-διάλεξη κουίζ](https://ff-quizzes.netlify.app/en/ai/quiz/3) -Στις πρώτες μέρες της Τεχνητής Νοημοσύνης, η προσέγγιση από πάνω προς τα κάτω για τη δημιουργία ευφυών συστημάτων (συζητήθηκε στο προηγούμενο μάθημα) ήταν δημοφιλής. Η ιδέα ήταν να εξαχθεί η γνώση από τους ανθρώπους σε μια μορφή που μπορεί να διαβαστεί από μηχανές και στη συνέχεια να χρησιμοποιηθεί για την αυτόματη επίλυση προβλημάτων. Αυτή η προσέγγιση βασίστηκε σε δύο μεγάλες ιδέες: +Στις πρώτες μέρες της ΤΝ, ο προσεγγιστικός τρόπος κορυφής προς τα κάτω για τη δημιουργία ευφυών συστημάτων (που συζητήθηκε στο προηγούμενο μάθημα) ήταν δημοφιλής. Η ιδέα ήταν να εξαχθεί η γνώση από ανθρώπους σε κάποια μορφή που να μπορεί να διαβαστεί από μηχανή, και στη συνέχεια να χρησιμοποιηθεί για να λύσει αυτόματα προβλήματα. Αυτή η προσέγγιση βασίστηκε σε δύο μεγάλα ιδέες: * Αναπαράσταση Γνώσης -* Συλλογιστική +* Λογική Σκέψη (Συλλογιστική) ## Αναπαράσταση Γνώσης -Ένα από τα σημαντικά στοιχεία στη Συμβολική Τεχνητή Νοημοσύνη είναι η **γνώση**. Είναι σημαντικό να διαχωρίσουμε τη γνώση από την *πληροφορία* ή τα *δεδομένα*. Για παράδειγμα, μπορεί να πει κανείς ότι τα βιβλία περιέχουν γνώση, επειδή μπορεί να μελετήσει κανείς βιβλία και να γίνει ειδικός. Ωστόσο, αυτό που περιέχουν τα βιβλία ονομάζεται στην πραγματικότητα *δεδομένα*, και με την ανάγνωση των βιβλίων και την ενσωμάτωση αυτών των δεδομένων στο μοντέλο μας για τον κόσμο, μετατρέπουμε αυτά τα δεδομένα σε γνώση. +Μια από τις σημαντικές έννοιες στη Συμβολική ΤΝ είναι η **γνώση**. Είναι σημαντικό να διαχωρίζουμε την γνώση από την *πληροφορία* ή τα *δεδομένα*. Για παράδειγμα, μπορεί κάποιος να πει ότι τα βιβλία περιέχουν γνώση, επειδή μπορεί κανείς να μελετήσει τα βιβλία και να γίνει ειδικός. Ωστόσο, αυτό που πραγματικά περιέχουν τα βιβλία ονομάζεται *δεδομένα*, και διαβάζοντας τα βιβλία και ενσωματώνοντας αυτά τα δεδομένα στο μοντέλο κόσμου μας μετατρέπουμε αυτά τα δεδομένα σε γνώση. -> ✅ **Γνώση** είναι κάτι που περιέχεται στο μυαλό μας και αντιπροσωπεύει την κατανόησή μας για τον κόσμο. Αποκτάται μέσω μιας ενεργής διαδικασίας **μάθησης**, η οποία ενσωματώνει κομμάτια πληροφοριών που λαμβάνουμε στο ενεργό μοντέλο μας για τον κόσμο. +> ✅ **Η γνώση** είναι κάτι που περιέχεται στο κεφάλι μας και αντιπροσωπεύει την κατανόησή μας για τον κόσμο. Αποκτάται μέσω μιας ενεργής διαδικασίας **μάθησης**, η οποία ενσωματώνει κομμάτια πληροφορίας που λαμβάνουμε στο ενεργό μοντέλο μας του κόσμου. -Συνήθως, δεν ορίζουμε αυστηρά τη γνώση, αλλά την ευθυγραμμίζουμε με άλλες συναφείς έννοιες χρησιμοποιώντας την [Πυραμίδα DIKW](https://en.wikipedia.org/wiki/DIKW_pyramid). Περιλαμβάνει τις εξής έννοιες: +Στις περισσότερες περιπτώσεις, δεν ορίζουμε αυστηρά τη γνώση, αλλά την ευθυγραμμίζουμε με άλλες σχετικές έννοιες χρησιμοποιώντας την [πυραμίδα DIKW](https://en.wikipedia.org/wiki/DIKW_pyramid). Περιλαμβάνει τις εξής έννοιες: -* **Δεδομένα** είναι κάτι που αντιπροσωπεύεται σε φυσικά μέσα, όπως γραπτό κείμενο ή προφορικός λόγος. Τα δεδομένα υπάρχουν ανεξάρτητα από τους ανθρώπους και μπορούν να μεταδοθούν μεταξύ τους. -* **Πληροφορία** είναι το πώς ερμηνεύουμε τα δεδομένα στο μυαλό μας. Για παράδειγμα, όταν ακούμε τη λέξη *υπολογιστής*, έχουμε κάποια κατανόηση του τι είναι. -* **Γνώση** είναι η πληροφορία που ενσωματώνεται στο μοντέλο μας για τον κόσμο. Για παράδειγμα, μόλις μάθουμε τι είναι ένας υπολογιστής, αρχίζουμε να έχουμε κάποιες ιδέες για το πώς λειτουργεί, πόσο κοστίζει και για τι μπορεί να χρησιμοποιηθεί. Αυτό το δίκτυο αλληλένδετων εννοιών σχηματίζει τη γνώση μας. -* **Σοφία** είναι ένα ακόμη επίπεδο κατανόησης του κόσμου και αντιπροσωπεύει τη *μετα-γνώση*, π.χ. κάποια αντίληψη για το πώς και πότε πρέπει να χρησιμοποιείται η γνώση. +* **Δεδομένα** είναι κάτι που αντιπροσωπεύεται σε φυσικό μέσο, όπως γραπτό κείμενο ή προφορικές λέξεις. Τα δεδομένα υπάρχουν ανεξάρτητα από τους ανθρώπους και μπορούν να μεταβιβαστούν μεταξύ ανθρώπων. +* **Πληροφορία** είναι ο τρόπος με τον οποίο ερμηνεύουμε τα δεδομένα στο μυαλό μας. Για παράδειγμα, όταν ακούμε τη λέξη *υπολογιστής*, έχουμε κάποια κατανόηση του τι είναι. +* **Γνώση** είναι η πληροφορία που ενσωματώνεται στο μοντέλο του κόσμου μας. Για παράδειγμα, μόλις μάθουμε τι είναι ένας υπολογιστής, αρχίζουμε να έχουμε κάποιες ιδέες για το πώς λειτουργεί, πόσο κοστίζει και για ποιο σκοπό μπορεί να χρησιμοποιηθεί. Αυτό το δίκτυο αλληλοσυνδεδεμένων εννοιών σχηματίζει τη γνώση μας. +* **Σοφία** είναι ένα ακόμη επίπεδο της κατανόησής μας για τον κόσμο, και αντιπροσωπεύει τη *μετα-γνώση*, δηλαδή μια ιδέα για το πώς και πότε πρέπει να χρησιμοποιείται η γνώση. - + -*Εικόνα [από τη Wikipedia](https://commons.wikimedia.org/w/index.php?curid=37705247), By Longlivetheux - Own work, CC BY-SA 4.0* +*Εικόνα [από τη Wikipedia](https://commons.wikimedia.org/w/index.php?curid=37705247), Από Longlivetheux - Ιδιωτικό έργο, CC BY-SA 4.0* -Έτσι, το πρόβλημα της **αναπαράστασης γνώσης** είναι να βρεθεί ένας αποτελεσματικός τρόπος να αναπαρασταθεί η γνώση μέσα σε έναν υπολογιστή με τη μορφή δεδομένων, ώστε να είναι αυτόματα χρησιμοποιήσιμη. Αυτό μπορεί να θεωρηθεί ως ένα φάσμα: +Έτσι, το πρόβλημα της **αναπαράστασης γνώσης** είναι να βρεθεί ένας αποτελεσματικός τρόπος να αναπαρασταθεί η γνώση μέσα σε έναν υπολογιστή με τη μορφή δεδομένων, ώστε να μπορεί να χρησιμοποιηθεί αυτόματα. Αυτό μπορεί να θεωρηθεί ως ένα φάσμα: -![Φάσμα αναπαράστασης γνώσης](../../../../translated_images/el/knowledge-spectrum.b60df631852c0217.webp) +![Φάσμα αναπαράστασης γνώσης](../../../../../../translated_images/el/knowledge-spectrum.b60df631852c0217.webp) > Εικόνα από [Dmitry Soshnikov](http://soshnikov.com) -* Στα αριστερά, υπάρχουν πολύ απλοί τύποι αναπαραστάσεων γνώσης που μπορούν να χρησιμοποιηθούν αποτελεσματικά από υπολογιστές. Ο απλούστερος είναι ο αλγοριθμικός, όπου η γνώση αναπαρίσταται από ένα πρόγραμμα υπολογιστή. Ωστόσο, αυτός δεν είναι ο καλύτερος τρόπος αναπαράστασης γνώσης, επειδή δεν είναι ευέλικτος. Η γνώση στο μυαλό μας συχνά δεν είναι αλγοριθμική. -* Στα δεξιά, υπάρχουν αναπαραστάσεις όπως το φυσικό κείμενο. Είναι η πιο ισχυρή μορφή, αλλά δεν μπορεί να χρησιμοποιηθεί για αυτόματη συλλογιστική. +* Στα αριστερά υπάρχουν πολύ απλοί τύποι αναπαραστάσεων γνώσης που μπορούν να χρησιμοποιηθούν αποτελεσματικά από υπολογιστές. Ο απλούστερος είναι ο αλγοριθμικός, όταν η γνώση αναπαρίσταται από ένα πρόγραμμα υπολογιστή. Αυτό, όμως, δεν είναι ο καλύτερος τρόπος αναπαράστασης γνώσης, επειδή δεν είναι ευέλικτος. Η γνώση στο κεφάλι μας συχνά δεν είναι αλγοριθμική. +* Στα δεξιά υπάρχουν αναπαραστάσεις όπως το φυσικό κείμενο. Είναι η πιο ισχυρή, αλλά δεν μπορεί να χρησιμοποιηθεί για αυτόματη συλλογιστική. -> ✅ Σκεφτείτε για ένα λεπτό πώς αναπαριστάτε τη γνώση στο μυαλό σας και τη μετατρέπετε σε σημειώσεις. Υπάρχει κάποια συγκεκριμένη μορφή που σας βοηθά στην απομνημόνευση; +> ✅ Σκεφτείτε για μια στιγμή πώς αναπαριστάτε τη γνώση στο κεφάλι σας και πώς την μετατρέπετε σε σημειώσεις. Υπάρχει κάποια συγκεκριμένη μορφή που λειτουργεί καλά για εσάς και βοηθά στη διατήρηση; -## Κατηγοριοποίηση Αναπαραστάσεων Γνώσης Υπολογιστών +## Ταξινόμηση Αναπαραστάσεων Γνώσης Υπολογιστών -Μπορούμε να κατηγοριοποιήσουμε διαφορετικές μεθόδους αναπαράστασης γνώσης υπολογιστών στις εξής κατηγορίες: +Μπορούμε να κατηγοριοποιήσουμε τις διάφορες μεθόδους αναπαράστασης γνώσης υπολογιστών στις παρακάτω κατηγορίες: -* **Δικτυακές αναπαραστάσεις** βασίζονται στο γεγονός ότι έχουμε ένα δίκτυο αλληλένδετων εννοιών στο μυαλό μας. Μπορούμε να προσπαθήσουμε να αναπαραγάγουμε τα ίδια δίκτυα ως γράφημα μέσα σε έναν υπολογιστή - ένα λεγόμενο **σημασιολογικό δίκτυο**. +* **Δικτυακές αναπαραστάσεις** βασίζονται στο γεγονός ότι έχουμε ένα δίκτυο αλληλοσυνδεδεμένων εννοιών μέσα στο κεφάλι μας. Μπορούμε να προσπαθήσουμε να αναπαραστήσουμε τα ίδια δίκτυα ως γράφο μέσα σε έναν υπολογιστή - το λεγόμενο **σημασιολογικό δίκτυο**. -1. **Τρίπλες Αντικειμένου-Χαρακτηριστικού-Τιμής** ή **ζεύγη χαρακτηριστικού-τιμής**. Επειδή ένα γράφημα μπορεί να αναπαρασταθεί μέσα σε έναν υπολογιστή ως λίστα κόμβων και ακμών, μπορούμε να αναπαραστήσουμε ένα σημασιολογικό δίκτυο με μια λίστα τρίπλων, που περιέχουν αντικείμενα, χαρακτηριστικά και τιμές. Για παράδειγμα, δημιουργούμε τα εξής τρίπλα για γλώσσες προγραμματισμού: +1. **Τριπλέτες Αντικείμενο-Χαρακτηριστικό-Τιμή** ή **Ζεύγη Χαρακτηριστικό-Τιμή**. Εφόσον ένας γράφος μπορεί να αναπαρασταθεί μέσα σε υπολογιστή ως λίστα από κόμβους και ακμές, μπορούμε να αναπαραστήσουμε ένα σημασιολογικό δίκτυο με μια λίστα τριπλετών που περιέχουν αντικείμενα, χαρακτηριστικά και τιμές. Για παράδειγμα, κατασκευάζουμε τις παρακάτω τριπλέτες για τις γλώσσες προγραμματισμού: Αντικείμενο | Χαρακτηριστικό | Τιμή ------------|----------------|------ -Python | είναι | Μη Τυποποιημένη Γλώσσα -Python | εφευρέθηκε από | Guido van Rossum -Python | σύνταξη μπλοκ | εσοχή -Μη Τυποποιημένη Γλώσσα | δεν έχει | ορισμούς τύπων +Python | είναι | Αδόμητη-Γλώσσα +Python | εφευρέθηκε-από | Guido van Rossum +Python | σύνταξη-μπλοκ | εσοχή +Αδόμητη-Γλώσσα | δεν-έχει | ορισμούς-τύπων -> ✅ Σκεφτείτε πώς μπορούν να χρησιμοποιηθούν τρίπλες για την αναπαράσταση άλλων τύπων γνώσης. +> ✅ Σκεφτείτε πώς οι τριπλέτες μπορούν να χρησιμοποιηθούν για να αναπαραστήσουν άλλους τύπους γνώσης. -2. **Ιεραρχικές αναπαραστάσεις** δίνουν έμφαση στο γεγονός ότι συχνά δημιουργούμε μια ιεραρχία αντικειμένων στο μυαλό μας. Για παράδειγμα, γνωρίζουμε ότι το καναρίνι είναι πουλί, και όλα τα πουλιά έχουν φτερά. Έχουμε επίσης κάποια ιδέα για το χρώμα που συνήθως έχει ένα καναρίνι και για την ταχύτητα πτήσης του. +2. **Ιεραρχικές αναπαραστάσεις** τονίζουν το γεγονός ότι συχνά δημιουργούμε μια ιεραρχία αντικειμένων στο κεφάλι μας. Για παράδειγμα, ξέρουμε ότι το καναρίνι είναι πουλί, και όλα τα πουλιά έχουν φτερά. Έχουμε επίσης κάποια ιδέα για το συνήθη χρώμα ενός καναρινιού και την ταχύτητα πτήσης τους. - - **Αναπαράσταση πλαισίων** βασίζεται στην αναπαράσταση κάθε αντικειμένου ή κατηγορίας αντικειμένων ως **πλαίσιο** που περιέχει **υποδοχές**. Οι υποδοχές έχουν πιθανές προεπιλεγμένες τιμές, περιορισμούς τιμών ή αποθηκευμένες διαδικασίες που μπορούν να κληθούν για να ληφθεί η τιμή μιας υποδοχής. Όλα τα πλαίσια σχηματίζουν μια ιεραρχία παρόμοια με την ιεραρχία αντικειμένων στις γλώσσες προγραμματισμού αντικειμένων. - - **Σενάρια** είναι ειδικά είδη πλαισίων που αναπαριστούν σύνθετες καταστάσεις που μπορούν να εξελιχθούν με την πάροδο του χρόνου. + - Η **αναπαράσταση πλαισίου (frame representation)** βασίζεται στην αναπαράσταση κάθε αντικειμένου ή κλάσης αντικειμένων ως **πλαίσιο (frame)** που περιέχει **θέσεις (slots)**. Οι θέσεις έχουν πιθανές προεπιλεγμένες τιμές, περιορισμούς τιμών, ή αποθηκευμένες διαδικασίες που μπορούν να κληθούν για να πάρουμε την τιμή μιας θέσης. Όλα τα πλαίσια σχηματίζουν μια ιεραρχία παρόμοια με την ιεραρχία αντικειμένων στις αντικειμενοστραφείς γλώσσες προγραμματισμού. + - Τα **σενάρια (scenarios)** είναι ειδικός τύπος πλαισίων που αναπαριστούν πολύπλοκες καταστάσεις που μπορούν να εξελιχθούν με τον χρόνο. **Python** -Υποδοχή | Τιμή | Προεπιλεγμένη τιμή | Διάστημα | ----------|------|--------------------|----------| +Θέση | Τιμή | Προεπιλεγμένη Τιμή | Διάστημα | +-----|-------|---------------------|----------| Όνομα | Python | | | -Είναι-Α | Μη Τυποποιημένη Γλώσσα | | | -Περίπτωση Μεταβλητής | | CamelCase | | +Είναι-Τύπος | Αδόμητη-Γλώσσα | | | +Τύπος Μεταβλητής | | CamelCase | | Μήκος Προγράμματος | | | 5-5000 γραμμές | Σύνταξη Μπλοκ | Εσοχή | | | -3. **Διαδικαστικές αναπαραστάσεις** βασίζονται στην αναπαράσταση γνώσης μέσω μιας λίστας ενεργειών που μπορούν να εκτελεστούν όταν προκύψει μια συγκεκριμένη συνθήκη. - - Οι κανόνες παραγωγής είναι δηλώσεις αν-τότε που μας επιτρέπουν να βγάζουμε συμπεράσματα. Για παράδειγμα, ένας γιατρός μπορεί να έχει έναν κανόνα που λέει ότι **ΑΝ** ένας ασθενής έχει υψηλό πυρετό **Ή** υψηλό επίπεδο C-αντιδρώσας πρωτεΐνης σε εξέταση αίματος **ΤΟΤΕ** έχει φλεγμονή. Μόλις συναντήσουμε μία από τις συνθήκες, μπορούμε να βγάλουμε ένα συμπέρασμα για τη φλεγμονή και στη συνέχεια να το χρησιμοποιήσουμε για περαιτέρω συλλογιστική. - - Οι αλγόριθμοι μπορούν να θεωρηθούν μια άλλη μορφή διαδικαστικής αναπαράστασης, αν και σχεδόν ποτέ δεν χρησιμοποιούνται άμεσα σε συστήματα βασισμένα στη γνώση. +3. **Διαδικασιακές αναπαραστάσεις** βασίζονται στην αναπαράσταση της γνώσης με μια λίστα ενεργειών που μπορούν να εκτελεστούν όταν συμβεί κάποιο συγκεκριμένο γεγονός. + - Οι κανόνες παραγωγής είναι δηλώσεις αν-τότε που μας επιτρέπουν να βγάζουμε συμπεράσματα. Για παράδειγμα, ένας γιατρός μπορεί να έχει έναν κανόνα που λέει ότι **ΑΝ** ένας ασθενής έχει υψηλό πυρετό **Η** υψηλό επίπεδο C-αντιδρώσας πρωτεΐνης σε εξέταση αίματος **ΤΟΤΕ** έχει φλεγμονή. Μόλις συναντήσουμε μία από τις συνθήκες, μπορούμε να βγάλουμε το συμπέρασμα για τη φλεγμονή και μετά να το χρησιμοποιήσουμε σε περαιτέρω συλλογισμούς. + - Οι αλγόριθμοι μπορούν να θεωρηθούν ως μια άλλη μορφή διαδικασιακής αναπαράστασης, αν και σχεδόν ποτέ δεν χρησιμοποιούνται απευθείας σε συστήματα βασισμένα σε γνώση. -4. **Λογική** προτάθηκε αρχικά από τον Αριστοτέλη ως τρόπος αναπαράστασης της καθολικής ανθρώπινης γνώσης. - - Η Λογική Κατηγορημάτων ως μαθηματική θεωρία είναι πολύ πλούσια για να είναι υπολογίσιμη, επομένως χρησιμοποιείται συνήθως κάποιο υποσύνολό της, όπως οι ρήτρες Horn που χρησιμοποιούνται στο Prolog. - - Η Περιγραφική Λογική είναι μια οικογένεια λογικών συστημάτων που χρησιμοποιούνται για την αναπαράσταση και τη συλλογιστική σχετικά με ιεραρχίες αντικειμένων και κατανεμημένες αναπαραστάσεις γνώσης όπως το *σημασιολογικό ιστό*. +4. **Λογική** προτάθηκε αρχικά από τον Αριστοτέλη ως τρόπος αναπαράστασης της παγκόσμιας ανθρώπινης γνώσης. + - Η Προτασιακή Λογική ως μαθηματική θεωρία είναι πολύ πλούσια για να είναι υπολογίσιμη, γι' αυτό χρησιμοποιούνται συνήθως υποσύνολα της, όπως οι ρητές Horn που χρησιμοποιούνται στο Prolog. + - Η Περιγραφική Λογική είναι μια οικογένεια λογικών συστημάτων που χρησιμοποιούνται για να αναπαραστήσουν και να συλλογιστούν για ιεραρχίες αντικειμένων και διανεμημένες αναπαραστάσεις γνώσης όπως ο *σημασιολογικός ιστός*. -## Συστήματα Ειδικών +## Ειδικά Συστήματα -Μία από τις πρώτες επιτυχίες της συμβολικής Τεχνητής Νοημοσύνης ήταν τα λεγόμενα **συστήματα ειδικών** - υπολογιστικά συστήματα που σχεδιάστηκαν για να λειτουργούν ως ειδικός σε κάποιο περιορισμένο πεδίο προβλημάτων. Βασίζονταν σε μια **βάση γνώσης** που εξαγόταν από έναν ή περισσότερους ανθρώπινους ειδικούς και περιείχαν μια **μηχανή συλλογιστικής** που εκτελούσε κάποια συλλογιστική πάνω σε αυτήν. +Μια από τις πρώτες επιτυχίες της συμβολικής ΤΝ ήταν τα λεγόμενα **ειδικά συστήματα** - υπολογιστικά συστήματα σχεδιασμένα να λειτουργούν ως ειδικοί σε έναν περιορισμένο τομέα προβλημάτων. Βασίζονταν σε μια **βάση γνώσης** που εξήχθη από έναν ή περισσότερους ανθρώπινους ειδικούς, και περιείχαν μια **μηχανή συλλογιστικής** που εκτελούσε συλλογισμούς πάνω σε αυτήν. -![Αρχιτεκτονική Ανθρώπου](../../../../translated_images/el/arch-human.5d4d35f1bba3ab1c.webp) | ![Αρχιτεκτονική Συστήματος Βασισμένου στη Γνώση](../../../../translated_images/el/arch-kbs.3ec5c150b09fa8da.webp) ----------------------------------------------|------------------------------------------------ -Απλοποιημένη δομή του ανθρώπινου νευρικού συστήματος | Αρχιτεκτονική ενός συστήματος βασισμένου στη γνώση +![Αρχιτεκτονική Ανθρώπου](../../../../../../translated_images/el/arch-human.5d4d35f1bba3ab1c.webp) | ![Σύστημα Βασισμένο σε Γνώση](../../../../../../translated_images/el/arch-kbs.3ec5c150b09fa8da.webp) +-------------------------------------------------------|---------------------------------------------- +Απλοποιημένη δομή του ανθρώπινου νευρικού συστήματος | Αρχιτεκτονική συστήματος βασισμένου σε γνώση -Τα συστήματα ειδικών είναι δομημένα όπως το ανθρώπινο σύστημα συλλογιστικής, το οποίο περιέχει **βραχυπρόθεσμη μνήμη** και **μακροπρόθεσμη μνήμη**. Παρομοίως, στα συστήματα βασισμένα στη γνώση διακρίνουμε τα εξής στοιχεία: +Τα ειδικά συστήματα χτίζονται όπως το ανθρώπινο σύστημα συλλογιστικής, το οποίο περιέχει **βραχυπρόθεσμη μνήμη** και **μακροπρόθεσμη μνήμη**. Παρομοίως, σε συστήματα βασισμένα σε γνώση διακρίνουμε τα εξής συστατικά: -* **Μνήμη προβλήματος**: περιέχει τη γνώση για το πρόβλημα που λύνεται αυτή τη στιγμή, π.χ. τη θερμοκρασία ή την αρτηριακή πίεση ενός ασθενούς, αν έχει φλεγμονή ή όχι, κλπ. Αυτή η γνώση ονομάζεται επίσης **στατική γνώση**, επειδή περιέχει μια στιγμιότυπο του τι γνωρίζουμε αυτή τη στιγμή για το πρόβλημα - την αποκαλούμενη *κατάσταση προβλήματος*. -* **Βάση γνώσης**: αντιπροσωπεύει τη μακροπρόθεσμη γνώση για ένα πεδίο προβλημάτων. Εξάγεται χειροκίνητα από ανθρώπινους ειδικούς και δεν αλλάζει από συμβουλή σε συμβουλή. Επειδή μας επιτρέπει να πλοηγηθούμε από μία κατάσταση προβλήματος σε άλλη, ονομάζεται επίσης **δυναμική γνώση**. -* **Μηχανή συλλογιστικής**: ενορχηστρώνει όλη τη διαδικασία αναζήτησης στον χώρο κατάστασης προβλήματος, κάνοντας ερωτήσεις στον χρήστη όταν είναι απαραίτητο. Είναι επίσης υπεύθυνη για την εύρεση των σωστών κανόνων που πρέπει να εφαρμοστούν σε κάθε κατάσταση. +* **Μνήμη προβλήματος**: περιέχει τη γνώση για το πρόβλημα που επιλύεται αυτή τη στιγμή, π.χ. τη θερμοκρασία ή την αρτηριακή πίεση ενός ασθενούς, αν έχει φλεγμονή ή όχι κλπ. Αυτή η γνώση ονομάζεται επίσης **στατική γνώση**, επειδή περιέχει ένα στιγμιότυπο του τι γνωρίζουμε επί του παρόντος για το πρόβλημα - την αποκαλούμενη *κατάσταση προβλήματος*. +* **Βάση γνώσης**: αναπαριστά μακροπρόθεσμη γνώση για έναν τομέα προβλημάτων. Εξάγεται χειροκίνητα από ανθρώπινους ειδικούς και δεν αλλάζει από διαβούλευση σε διαβούλευση. Επειδή επιτρέπει την πλοήγηση από μία κατάσταση προβλήματος σε άλλη, ονομάζεται επίσης **δυναμική γνώση**. +* **Μηχανή συλλογιστικής**: συντονίζει ολόκληρη τη διαδικασία αναζήτησης στο χώρο καταστάσεων προβλήματος, κάνοντας ερωτήσεις στον χρήστη όταν χρειάζεται. Είναι επίσης υπεύθυνη για την εύρεση των σωστών κανόνων που πρέπει να εφαρμοστούν σε κάθε κατάσταση. -Ως παράδειγμα, ας εξετάσουμε το εξής σύστημα ειδικών για τον προσδιορισμό ενός ζώου βάσει των φυσικών του χαρακτηριστικών: +Ένα παράδειγμα είναι το παρακάτω ειδικό σύστημα για την αναγνώριση ενός ζώου βάσει των φυσικών του χαρακτηριστικών: -![Δέντρο AND-OR](../../../../translated_images/el/AND-OR-Tree.5592d2c70187f283.webp) +![Δέντρο AND-OR](../../../../../../translated_images/el/AND-OR-Tree.5592d2c70187f283.webp) > Εικόνα από [Dmitry Soshnikov](http://soshnikov.com) @@ -121,53 +121,78 @@ OR (animal has sharp teeth THEN the animal is a carnivore ``` -Μπορείτε να παρατηρήσετε ότι κάθε συνθήκη στην αριστερή πλευρά του κανόνα και η ενέργεια είναι ουσιαστικά τρίπλες αντικειμένου-χαρακτηριστικού-τιμής (OAV). Η **εργαζόμενη μνήμη** περιέχει το σύνολο των τρίπλων OAV που αντιστοιχούν στο πρόβλημα που λύνεται αυτή τη στιγμή. Μια **μηχανή κανόνων** αναζητά κανόνες για τους οποίους μια συνθήκη ικανοποιείται και τους εφαρμόζει, προσθέτοντας μια άλλη τρίπλα στην εργαζόμενη μνήμη. +Μπορείτε να παρατηρήσετε ότι κάθε συνθήκη στην αριστερή πλευρά του κανόνα και η ενέργεια είναι ουσιαστικά τριπλέτες αντικείμενο-χαρακτηριστικό-τιμή (OAV). Η **εργαζόμενη μνήμη** περιέχει το σύνολο των τριπλετών OAV που αντιστοιχούν στο πρόβλημα που λύνεται αυτή τη στιγμή. Μια **μηχανή κανόνων** ψάχνει για κανόνες των οποίων η συνθήκη ικανοποιείται και τους εφαρμόζει, προσθέτοντας μια ακόμη τριπλέτα στην εργαζόμενη μνήμη. -> ✅ Δημιουργήστε το δικό σας δέντρο AND-OR για ένα θέμα που σας ενδιαφέρει! +> ✅ Γράψτε το δικό σας δέντρο AND-OR σε ένα θέμα που σας ενδιαφέρει! -### Προώθηση vs. Οπισθοδρόμηση Συλλογιστικής +### Προώθηση έναντι Οπισθοδρομικής Συλλογιστικής -Η διαδικασία που περιγράφηκε παραπάνω ονομάζεται **προώθηση συλλογιστικής**. Ξεκινά με κάποια αρχικά δεδομένα για το πρόβλημα που είναι διαθέσιμα στην εργαζόμενη μνήμη και στη συνέχεια εκτελεί τον εξής βρόχο συλλογιστικής: +Η παραπάνω διαδικασία ονομάζεται **προώθηση (forward inference)**. Ξεκινάει με κάποια αρχικά δεδομένα για το πρόβλημα που είναι διαθέσιμα στην εργαζόμενη μνήμη, και έπειτα εκτελεί τον εξής βρόχο συλλογιστικής: -1. Αν το στόχο χαρακτηριστικό υπάρχει στην εργαζόμενη μνήμη - σταματήστε και δώστε το αποτέλεσμα -2. Αναζητήστε όλους τους κανόνες των οποίων η συνθήκη ικανοποιείται αυτή τη στιγμή - αποκτήστε το **σύνολο σύγκρουσης** κανόνων. -3. Εκτελέστε **επίλυση σύγκρουσης** - επιλέξτε έναν κανόνα που θα εκτελεστεί σε αυτό το βήμα. Μπορεί να υπάρχουν διαφορετικές στρατηγικές επίλυσης σύγκρουσης: +1. Αν το ζητούμενο χαρακτηριστικό υπάρχει στην εργαζόμενη μνήμη - σταματήστε και δώστε το αποτέλεσμα +2. Ψάξτε για όλους τους κανόνες των οποίων η συνθήκη ικανοποιείται - πάρτε το **σύνολο σύγκρουσης** των κανόνων. +3. Εκτελέστε **επίλυση σύγκρουσης** - επιλέξτε έναν κανόνα που θα εκτελεστεί σε αυτό το βήμα. Υπάρχουν διάφορες στρατηγικές επίλυσης σύγκρουσης: - Επιλέξτε τον πρώτο εφαρμόσιμο κανόνα στη βάση γνώσης - Επιλέξτε έναν τυχαίο κανόνα - - Επιλέξτε έναν *πιο συγκεκριμένο* κανόνα, δηλαδή αυτόν που πληροί τις περισσότερες συνθήκες στην "αριστερή πλευρά" (LHS) -4. Εφαρμόστε τον επιλεγμένο κανόνα και εισάγετε ένα νέο κομμάτι γνώσης στην κατάσταση προβλήματος + - Επιλέξτε έναν *πιο συγκεκριμένο* κανόνα, δηλαδή αυτόν που ικανοποιεί τις περισσότερες συνθήκες στο "αριστερό μέρος" (LHS) +4. Εφαρμόστε τον επιλεγμένο κανόνα και εισάγετε νέο κομμάτι γνώσης στην κατάσταση προβλήματος 5. Επαναλάβετε από το βήμα 1. -Ωστόσο, σε ορισμένες περιπτώσεις μπορεί να θέλουμε να ξεκινήσουμε με άδεια γνώση για το πρόβλημα και να κάνουμε ερωτήσεις που θα μας βοηθήσουν να φτάσουμε στο συμπέρασμα. Για παράδειγμα, όταν κάνουμε ιατρική διάγνωση, συνήθως δεν εκτελούμε όλες τις ιατρικές αναλύσεις εκ των προτέρων πριν ξεκινήσουμε τη διάγνωση του ασθενούς. Αντίθετα, θέλουμε να εκτελέσουμε αναλύσεις όταν πρέπει να ληφθεί μια απόφαση. +Ωστόσο, σε ορισμένες περιπτώσεις μπορεί να θέλουμε να ξεκινήσουμε με καθαρή γνώση για το πρόβλημα και να κάνουμε ερωτήσεις που θα μας βοηθήσουν να καταλήξουμε στο συμπέρασμα. Για παράδειγμα, κατά τη διάγνωση ασθενειών, συνήθως δεν κάνουμε όλες τις ιατρικές εξετάσεις εκ των προτέρων πριν ξεκινήσουμε τη διάγνωση. Προτιμούμε να κάνουμε εξετάσεις όταν πρέπει να ληφθεί η απόφαση. -Αυτή η διαδικασία μπορεί να μοντελοποιηθεί χρησιμοποιώντας **οπισθοδρόμηση συλλογιστικής**. Καθοδηγείται από τον **στόχο** - την τιμή χαρακτηριστικού που αναζητούμε να βρούμε: +Αυτή η διαδικασία μπορεί να μοντελοποιηθεί με τη χρήση της **οπισθοδρομικής συλλογιστικής (backward inference)**. Οδηγείται από τον **στόχο** - την τιμή του χαρακτηριστικού που αναζητούμε: -1. Επιλέξτε όλους τους κανόνες που μπορούν να μας δώσουν την τιμή ενός στόχου (δηλαδή με τον στόχο στη δεξιά πλευρά (RHS)) - ένα σύνολο σύγκρουσης -1. Αν δεν υπάρχουν κανόνες για αυτό το χαρακτηριστικό ή υπάρχει κανόνας που λέει ότι πρέπει να ζητήσουμε την +1. Επιλέξτε όλους τους κανόνες που μπορούν να μας δώσουν την τιμή του στόχου (δηλαδή με τον στόχο στη δεξιά πλευρά, RHS) - σύνολο σύγκρουσης +1. Αν δεν υπάρχουν κανόνες για αυτό το χαρακτηριστικό, ή υπάρχει κανόνας που λέει ότι πρέπει να ρωτήσουμε τον χρήστη - ρωτήστε το, αλλιώς: +1. Χρησιμοποιήστε στρατηγική επίλυσης σύγκρουσης για να επιλέξετε έναν κανόνα που θα χρησιμοποιήσουμε ως *υπόθεση* - θα προσπαθήσουμε να την αποδείξουμε +1. Επαναληπτικά επαναλάβετε τη διαδικασία για όλα τα χαρακτηριστικά στο LHS του κανόνα, προσπαθώντας να τα αποδείξετε ως στόχους +1. Αν σε κάποιο σημείο η διαδικασία αποτύχει - χρησιμοποιήστε άλλον κανόνα στο βήμα 3. + +> ✅ Σε ποιες περιπτώσεις είναι πιο κατάλληλη η προώθηση (forward inference); Και η οπισθοδρομική συλλογιστική; + +### Υλοποίηση Ειδικών Συστημάτων + +Τα ειδικά συστήματα μπορούν να υλοποιηθούν με διάφορα εργαλεία: + +* Προγραμματίζοντας τα απευθείας σε κάποια γλώσσα προγραμματισμού υψηλού επιπέδου. Αυτό δεν είναι η καλύτερη ιδέα, επειδή το κύριο πλεονέκτημα ενός συστήματος βασισμένου σε γνώση είναι ότι η γνώση διαχωρίζεται από τη συλλογιστική και ενδεχομένως ο ειδικός του τομέα προβλημάτων θα πρέπει να μπορεί να γράψει κανόνες χωρίς να κατανοεί τις λεπτομέρειες της διαδικασίας συλλογιστικής. +* Χρησιμοποιώντας ένα **κέλυφος ειδικού συστήματος (expert systems shell)**, δηλαδή ένα σύστημα σχεδιασμένο ειδικά για να γεμίζει με γνώση χρησιμοποιώντας κάποια γλώσσα αναπαράστασης γνώσης. + +## ✍️ Άσκηση: Συλλογιστική για Ζώα + +Δείτε το [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) για ένα παράδειγμα υλοποίησης ειδικού συστήματος με προώθηση και οπισθοδρομική συλλογιστική. + +> **Σημείωση**: Αυτό το παράδειγμα είναι αρκετά απλό, και δίνει μόνο την ιδέα του πώς μοιάζει ένα ειδικό σύστημα. Όταν αρχίζετε να δημιουργείτε τέτοιο σύστημα, θα παρατηρήσετε *έξυπνη* συμπεριφορά μόνο αφού φτάσετε έναν αριθμό κανόνων γύρω στους 200+. Σε κάποιο σημείο, οι κανόνες γίνονται πολύ περίπλοκοι για να θυμάστε όλους, και τότε μπορεί να αναρωτηθείτε γιατί το σύστημα παίρνει ορισμένες αποφάσεις. Ωστόσο, τα σημαντικά χαρακτηριστικά των συστημάτων βασισμένων σε γνώση είναι ότι μπορείτε πάντα να *εξηγήσετε* ακριβώς πώς λήφθηκαν οποιεσδήποτε αποφάσεις. + +## Οντολογίες και ο Σημασιολογικός Ιστός + +Στο τέλος του 20ού αιώνα υπήρξε μια πρωτοβουλία να χρησιμοποιηθεί η αναπαράσταση γνώσης για να σχολιαστούν πόροι του Διαδικτύου, ώστε να είναι δυνατή η εύρεση πόρων που αντιστοιχούν σε πολύ συγκεκριμένα ερωτήματα. Αυτή η πρωτοβουλία ονομάστηκε **Σημασιολογικός Ιστός (Semantic Web)**, και βασίστηκε σε αρκετές έννοιες: + +- Μια ειδική αναπαράσταση γνώσης βασισμένη στην **[περιγραφική λογική](https://en.wikipedia.org/wiki/Description_logic)** (DL). Είναι παρόμοια με την αναπαράσταση γνώσης με πλαίσια, επειδή δημιουργεί μια ιεραρχία αντικειμένων με ιδιότητες, αλλά έχει τυπική λογική σημασιολογία και συλλογιστική. Υπάρχει ολόκληρη οικογένεια DL που ισορροπεί μεταξύ της εκφραστικότητας και της αλγοριθμικής πολυπλοκότητας της συλλογιστικής. +- Διανεμημένη αναπαράσταση γνώσης, όπου όλες οι έννοιες αναπαρίστανται με έναν παγκόσμιο αναγνωριστικό URI, καθιστώντας δυνατή τη δημιουργία ιεραρχιών γνώσης που εκτείνονται στο διαδίκτυο. - Μια οικογένεια γλωσσών βασισμένων σε XML για περιγραφή γνώσης: RDF (Resource Description Framework), RDFS (RDF Schema), OWL (Ontology Web Language). -Ένα βασικό στοιχείο στον Σημασιολογικό Ιστό είναι η έννοια της **Οντολογίας**. Αναφέρεται σε μια σαφή περιγραφή ενός πεδίου προβλήματος χρησιμοποιώντας κάποια επίσημη αναπαράσταση γνώσης. Η πιο απλή οντολογία μπορεί να είναι απλώς μια ιεραρχία αντικειμένων σε ένα πεδίο προβλήματος, αλλά πιο σύνθετες οντολογίες περιλαμβάνουν κανόνες που μπορούν να χρησιμοποιηθούν για συμπεράσματα. +Ένα βασικό πλαίσιο στο Σημασιολογικό Ιστό είναι η έννοια της **Οντολογίας**. Αναφέρεται σε μια ρητή προδιαγραφή ενός πεδίου προβλήματος χρησιμοποιώντας κάποια επίσημη αναπαράσταση γνώσης. Η πιο απλή οντολογία μπορεί να είναι απλώς μια ιεραρχία αντικειμένων σε ένα πεδίο προβλήματος, αλλά πιο πολύπλοκες οντολογίες θα περιλαμβάνουν κανόνες που μπορούν να χρησιμοποιηθούν για συμπερασμό. -Στον Σημασιολογικό Ιστό, όλες οι αναπαραστάσεις βασίζονται σε τριάδες. Κάθε αντικείμενο και κάθε σχέση προσδιορίζονται μοναδικά από το URI. Για παράδειγμα, αν θέλουμε να δηλώσουμε το γεγονός ότι αυτό το πρόγραμμα σπουδών AI αναπτύχθηκε από τον Dmitry Soshnikov την 1η Ιανουαρίου 2022 - εδώ είναι οι τριάδες που μπορούμε να χρησιμοποιήσουμε: +Στον σημασιολογικό ιστό, όλες οι αναπαραστάσεις βασίζονται σε τριάδες. Κάθε αντικείμενο και κάθε σχέση ταυτοποιούνται μοναδικά από το URI. Για παράδειγμα, αν θέλουμε να δηλώσουμε το γεγονός ότι αυτό το Πρόγραμμα Σπουδών Τεχνητής Νοημοσύνης έχει αναπτυχθεί από τον Dmitry Soshnikov την 1η Ιανουαρίου 2022 - εδώ είναι οι τριάδες που μπορούμε να χρησιμοποιήσουμε: - + ``` -http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 13, 2007” +http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 1, 2022” http://github.com/microsoft/ai-for-beginners http://purl.org/dc/elements/1.1/creator http://soshnikov.com ``` -> ✅ Εδώ `http://www.example.com/terms/creation-date` και `http://purl.org/dc/elements/1.1/creator` είναι κάποια γνωστά και ευρέως αποδεκτά URIs για την έκφραση των εννοιών *δημιουργός* και *ημερομηνία δημιουργίας*. +> ✅ Εδώ τα `http://www.example.com/terms/creation-date` και `http://purl.org/dc/elements/1.1/creator` είναι κάποια καλά γνωστά και καθολικά αποδεκτά URIs για να εκφράσουν τις έννοιες του *δημιουργού* και της *ημερομηνίας δημιουργίας*. -Σε μια πιο σύνθετη περίπτωση, αν θέλουμε να ορίσουμε μια λίστα δημιουργών, μπορούμε να χρησιμοποιήσουμε κάποιες δομές δεδομένων που ορίζονται στο RDF. +Σε μια πιο σύνθετη περίπτωση, αν θέλουμε να ορίσουμε μια λίστα δημιουργών, μπορούμε να χρησιμοποιήσουμε κάποιες δομές δεδομένων ορισμένες στο RDF. - + -> Τα παραπάνω διαγράμματα από τον [Dmitry Soshnikov](http://soshnikov.com) +> Διαγράμματα παραπάνω από τον [Dmitry Soshnikov](http://soshnikov.com) -Η πρόοδος στην κατασκευή του Σημασιολογικού Ιστού επιβραδύνθηκε κάπως λόγω της επιτυχίας των μηχανών αναζήτησης και των τεχνικών επεξεργασίας φυσικής γλώσσας, που επιτρέπουν την εξαγωγή δομημένων δεδομένων από κείμενο. Ωστόσο, σε ορισμένους τομείς εξακολουθούν να καταβάλλονται σημαντικές προσπάθειες για τη διατήρηση οντολογιών και βάσεων γνώσης. Μερικά έργα που αξίζει να σημειωθούν: +Η πρόοδος στην κατασκευή του Σημασιολογικού Ιστού επιβραδύνθηκε κάπως από την επιτυχία των μηχανών αναζήτησης και των τεχνικών επεξεργασίας φυσικής γλώσσας, οι οποίες επιτρέπουν την εξαγωγή δομημένων δεδομένων από κείμενο. Ωστόσο, σε ορισμένους τομείς εξακολουθούν να γίνονται σημαντικές προσπάθειες για τη διατήρηση οντολογιών και βάσεων γνώσης. Μερικά έργα που αξίζει να σημειωθούν: -* [WikiData](https://wikidata.org/) είναι μια συλλογή μηχανικά αναγνώσιμων βάσεων γνώσης που συνδέονται με τη Wikipedia. Τα περισσότερα δεδομένα εξάγονται από τα *InfoBoxes* της Wikipedia, κομμάτια δομημένου περιεχομένου μέσα στις σελίδες της Wikipedia. Μπορείτε να [κάνετε ερωτήματα](https://query.wikidata.org/) στο WikiData χρησιμοποιώντας SPARQL, μια ειδική γλώσσα ερωτημάτων για τον Σημασιολογικό Ιστό. Εδώ είναι ένα δείγμα ερωτήματος που εμφανίζει τα πιο δημοφιλή χρώματα ματιών στους ανθρώπους: +* [WikiData](https://wikidata.org/) είναι μια συλλογή μηχανικά αναγνώσιμων βάσεων γνώσης που σχετίζονται με τη Wikipedia. Τα περισσότερα δεδομένα εξορύσσονται από τα *InfoBoxes* της Wikipedia, κομμάτια δομημένου περιεχομένου μέσα στις σελίδες της Wikipedia. Μπορείτε να [κάνετε ερωτήματα](https://query.wikidata.org/) σε wikidata με SPARQL, μια ειδική γλώσσα ερωτημάτων για τον Σημασιολογικό Ιστό. Εδώ είναι ένα παράδειγμα ερωτήματος που εμφανίζει τα πιο δημοφιλή χρώματα ματιών ανάμεσα σε ανθρώπους: ```sparql #defaultView:BubbleChart @@ -183,45 +208,50 @@ GROUP BY ?eyeColorLabel * [DBpedia](https://www.dbpedia.org/) είναι μια άλλη προσπάθεια παρόμοια με το WikiData. -> ✅ Αν θέλετε να πειραματιστείτε με τη δημιουργία δικών σας οντολογιών ή με την εξερεύνηση υπαρχουσών, υπάρχει ένας εξαιρετικός οπτικός επεξεργαστής οντολογιών που ονομάζεται [Protégé](https://protege.stanford.edu/). Κατεβάστε τον ή χρησιμοποιήστε τον online. +> ✅ Αν θέλετε να πειραματιστείτε με τη δημιουργία των δικών σας οντολογιών ή να ανοίξετε υπάρχουσες οντολογίες, υπάρχει ένας εξαιρετικός οπτικός επεξεργαστής οντολογίας που ονομάζεται [Protégé](https://protege.stanford.edu/). Κατεβάστε τον ή χρησιμοποιήστε τον online. - + -*Ο επεξεργαστής Web Protégé ανοιχτός με την οντολογία της οικογένειας Romanov. Στιγμιότυπο από τον Dmitry Soshnikov* +*Ο επεξεργαστής Web Protégé ανοιχτός με την οντολογία της Οικογένειας Ρομάνοφ. Στιγμιότυπο οθόνης από τον Dmitry Soshnikov* -## ✍️ Άσκηση: Οντολογία Οικογένειας +## ✍️ Άσκηση: Μια Οντολογία Οικογένειας -Δείτε το [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) για ένα παράδειγμα χρήσης τεχνικών του Σημασιολογικού Ιστού για την εξαγωγή συμπερασμάτων σχετικά με οικογενειακές σχέσεις. Θα πάρουμε ένα οικογενειακό δέντρο που αναπαρίσταται σε κοινή μορφή GEDCOM και μια οντολογία οικογενειακών σχέσεων και θα δημιουργήσουμε ένα γράφημα όλων των οικογενειακών σχέσεων για ένα δεδομένο σύνολο ατόμων. -## Microsoft Concept Graph +Δείτε το [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) για ένα παράδειγμα χρήσης τεχνικών Σημασιολογικού Ιστού για να κάνετε συμπεράσματα σχετικά με οικογενειακές σχέσεις. Θα λάβουμε ένα οικογενειακό δέντρο που αναπαρίσταται στη συνηθισμένη μορφή GEDCOM και μια οντολογία οικογενειακών σχέσεων και θα δημιουργήσουμε ένα γράφημα όλων των οικογενειακών σχέσεων για ένα δοσμένο σύνολο ατόμων. -Στις περισσότερες περιπτώσεις, οι οντολογίες δημιουργούνται προσεκτικά με το χέρι. Ωστόσο, είναι επίσης δυνατό να **εξορύξουμε** οντολογίες από μη δομημένα δεδομένα, για παράδειγμα, από κείμενα φυσικής γλώσσας. +## Γράφημα Εννοιών Microsoft -Μια τέτοια προσπάθεια έγινε από το Microsoft Research και οδήγησε στο [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). +Στις περισσότερες περιπτώσεις, οι οντολογίες δημιουργούνται προσεκτικά με το χέρι. Ωστόσο, είναι επίσης δυνατό να **εξορυχθούν** οντολογίες από μη δομημένα δεδομένα, για παράδειγμα, από κείμενα φυσικής γλώσσας. -Πρόκειται για μια μεγάλη συλλογή οντοτήτων που ομαδοποιούνται χρησιμοποιώντας τη σχέση κληρονομικότητας `is-a`. Επιτρέπει την απάντηση ερωτήσεων όπως "Τι είναι η Microsoft;" - η απάντηση μπορεί να είναι κάτι σαν "μια εταιρεία με πιθανότητα 0.87, και ένα brand με πιθανότητα 0.75". +Μια τέτοια προσπάθεια έγινε από την Microsoft Research, και είχε ως αποτέλεσμα το [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). -Το Graph είναι διαθέσιμο είτε ως REST API είτε ως ένα μεγάλο αρχείο κειμένου που περιλαμβάνει όλα τα ζεύγη οντοτήτων. +Είναι μια μεγάλη συλλογή οντοτήτων ομαδοποιημένων χρησιμοποιώντας τη σχέση κληρονομικότητας `είναι-ένα`. Επιτρέπει την απάντηση σε ερωτήσεις όπως "Τι είναι η Microsoft;" - με απάντηση κάτι σαν "μια εταιρεία με πιθανότητα 0.87 και μια μάρκα με πιθανότητα 0.75". -## ✍️ Άσκηση: Γράφημα Εννοιών +Το Γράφημα είναι διαθέσιμο είτε ως REST API, είτε ως ένα μεγάλο αρχείο κειμένου για κατέβασμα που παραθέτει όλα τα ζεύγη οντοτήτων. -Δοκιμάστε το [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) notebook για να δείτε πώς μπορούμε να χρησιμοποιήσουμε το Microsoft Concept Graph για να ομαδοποιήσουμε ειδήσεις σε διάφορες κατηγορίες. +## ✍️ Άσκηση: Ένα Γράφημα Εννοιών + +Δοκιμάστε το σημειωματάριο [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) για να δείτε πώς μπορούμε να χρησιμοποιήσουμε το Microsoft Concept Graph για να ομαδοποιήσουμε ειδήσεις σε διάφορες κατηγορίες. ## Συμπέρασμα -Σήμερα, η Τεχνητή Νοημοσύνη συχνά θεωρείται συνώνυμο του *Machine Learning* ή των *Νευρωνικών Δικτύων*. Ωστόσο, ο άνθρωπος επιδεικνύει επίσης σαφή λογική, κάτι που αυτή τη στιγμή δεν αντιμετωπίζεται από τα νευρωνικά δίκτυα. Σε πραγματικά έργα, η σαφής λογική εξακολουθεί να χρησιμοποιείται για την εκτέλεση εργασιών που απαιτούν εξηγήσεις ή την ικανότητα να τροποποιηθεί η συμπεριφορά του συστήματος με ελεγχόμενο τρόπο. +Σήμερα, η Τεχνητή Νοημοσύνη συχνά θεωρείται συνώνυμο του *Machine Learning* ή των *Νευρωνικών Δικτύων*. Ωστόσο, ο άνθρωπος επιδεικνύει επίσης ρητό συλλογισμό, κάτι που προς το παρόν δεν καλύπτεται από τα νευρωνικά δίκτυα. Σε πραγματικά έργα, ο ρητός συλλογισμός εξακολουθεί να χρησιμοποιείται για την εκτέλεση εργασιών που απαιτούν εξηγήσεις, ή την ικανότητα να τροποποιείται η συμπεριφορά του συστήματος με ελεγχόμενο τρόπο. ## 🚀 Πρόκληση -Στο notebook Family Ontology που συνδέεται με αυτό το μάθημα, υπάρχει η ευκαιρία να πειραματιστείτε με άλλες οικογενειακές σχέσεις. Προσπαθήστε να ανακαλύψετε νέες συνδέσεις μεταξύ ατόμων στο οικογενειακό δέντρο. +Στο σημειωματάριο Οντολογίας Οικογένειας που σχετίζεται με αυτό το μάθημα, υπάρχει η ευκαιρία να πειραματιστείτε με άλλες οικογενειακές σχέσεις. Προσπαθήστε να ανακαλύψετε νέες συνδέσεις ανάμεσα σε ανθρώπους στο οικογενειακό δέντρο. -## [Κουίζ μετά το μάθημα](https://ff-quizzes.netlify.app/en/ai/quiz/4) +## [Κουίζ μετά τη διάλεξη](https://ff-quizzes.netlify.app/en/ai/quiz/4) -## Ανασκόπηση & Αυτομελέτη +## Ανασκόπηση & Αυτοδίδακτος -Κάντε έρευνα στο διαδίκτυο για να ανακαλύψετε περιοχές όπου οι άνθρωποι προσπάθησαν να ποσοτικοποιήσουν και να κωδικοποιήσουν τη γνώση. Ρίξτε μια ματιά στην Ταξινομία του Bloom και επιστρέψτε στην ιστορία για να μάθετε πώς οι άνθρωποι προσπάθησαν να κατανοήσουν τον κόσμο τους. Εξερευνήστε το έργο του Linnaeus για τη δημιουργία μιας ταξινομίας οργανισμών και παρατηρήστε τον τρόπο με τον οποίο ο Dmitri Mendeleev δημιούργησε έναν τρόπο για την περιγραφή και την ομαδοποίηση χημικών στοιχείων. Τι άλλα ενδιαφέροντα παραδείγματα μπορείτε να βρείτε; +Κάντε μια έρευνα στο διαδίκτυο για να ανακαλύψετε τομείς όπου οι άνθρωποι προσπάθησαν να ποσοτικοποιήσουν και να κωδικοποιήσουν τη γνώση. Ρίξτε μια ματιά στη Ταξινομία του Bloom, και επιστρέψτε στην ιστορία για να μάθετε πώς οι άνθρωποι προσπάθησαν να κατανοήσουν τον κόσμο τους. Εξερευνήστε το έργο του Linnaeus για τη δημιουργία μιας ταξονομίας οργανισμών, και παρατηρήστε τον τρόπο που ο Dmitri Mendeleev δημιούργησε έναν τρόπο για να περιγράφονται και να ομαδοποιούνται τα χημικά στοιχεία. Ποια άλλα ενδιαφέροντα παραδείγματα μπορείτε να βρείτε; -**Εργασία**: [Δημιουργήστε μια Οντολογία](assignment.md) +**Ανάθεση**: [Κατασκευή Οντολογίας](assignment.md) --- + +**Αποποίηση ευθυνών**: +Αυτό το έγγραφο έχει μεταφραστεί χρησιμοποιώντας την υπηρεσία μετάφρασης AI [Co-op Translator](https://github.com/Azure/co-op-translator). Παρόλο που καταβάλλουμε προσπάθεια για ακρίβεια, παρακαλούμε να λάβετε υπόψη ότι οι αυτόματες μεταφράσεις ενδέχεται να περιέχουν σφάλματα ή ανακρίβειες. Το πρωτότυπο έγγραφο στη μητρική του γλώσσα πρέπει να θεωρείται η αυθεντική πηγή. Για κρίσιμες πληροφορίες, συνιστάται η επαγγελματική μετάφραση από ανθρώπους. Δεν φέρουμε καμία ευθύνη για τυχόν παρεξηγήσεις ή λανθασμένες ερμηνείες που προκύπτουν από τη χρήση αυτής της μετάφρασης. + \ No newline at end of file diff --git a/translations/it/README.md b/translations/it/README.md index c90da403..a447842f 100644 --- a/translations/it/README.md +++ b/translations/it/README.md @@ -1,8 +1,8 @@ [Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Chinese (Simplified)](../zh/README.md) | [Chinese (Traditional, Hong Kong)](../hk/README.md) | [Chinese (Traditional, Macau)](../mo/README.md) | [Chinese (Traditional, Taiwan)](../tw/README.md) | [Croatian](../hr/README.md) | [Czech](../cs/README.md) | [Danish](../da/README.md) | [Dutch](../nl/README.md) | [Estonian](../et/README.md) | [Finnish](../fi/README.md) | [French](../fr/README.md) | [German](../de/README.md) | [Greek](../el/README.md) | [Hebrew](../he/README.md) | [Hindi](../hi/README.md) | [Hungarian](../hu/README.md) | [Indonesian](../id/README.md) | [Italian](./README.md) | [Japanese](../ja/README.md) | [Kannada](../kn/README.md) | [Korean](../ko/README.md) | [Lithuanian](../lt/README.md) | [Malay](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Nigerian Pidgin](../pcm/README.md) | [Norwegian](../no/README.md) | [Persian (Farsi)](../fa/README.md) | [Polish](../pl/README.md) | [Portuguese (Brazil)](../br/README.md) | [Portuguese (Portugal)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romanian](../ro/README.md) | [Russian](../ru/README.md) | [Serbian (Cyrillic)](../sr/README.md) | [Slovak](../sk/README.md) | [Slovenian](../sl/README.md) | [Spanish](../es/README.md) | [Swahili](../sw/README.md) | [Swedish](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Turkish](../tr/README.md) | [Ukrainian](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamese](../vi/README.md) -> **Preferisci clonare localmente?** +> **Preferisci Clonare Localmente?** -> Questo repository include più di 50 traduzioni linguistiche che aumentano significativamente la dimensione del download. Per clonare senza traduzioni, usa il sparse checkout: +> Questo repository include oltre 50 traduzioni che aumentano significativamente la dimensione del download. Per clonare senza le traduzioni, usa il sparse checkout: > ```bash > git clone --filter=blob:none --sparse https://github.com/microsoft/AI-For-Beginners.git > cd AI-For-Beginners > git sparse-checkout set --no-cone '/*' '!translations' '!translated_images' > ``` -> Questo ti fornirà tutto il necessario per completare il corso con un download molto più veloce. +> Questo ti dà tutto ciò di cui hai bisogno per completare il corso con un download molto più rapido. -**Se desideri che vengano supportate altre lingue di traduzione, sono elencate [qui](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** +**Se desideri supportare ulteriori lingue di traduzione, sono elencate [qui](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** ## Unisciti alla Comunità [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -## Cosa imparerai +## Cosa Imparerai -**[Mappa mentale del corso](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** +**[Mappa Mentale del Corso](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** In questo curriculum, imparerai: -* Diversi approcci all'Intelligenza Artificiale, incluso il vecchio e valido approccio simbolico con la **Rappresentazione della Conoscenza** e il ragionamento ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). -* **Reti Neurali** e **Deep Learning**, che sono al cuore dell'IA moderna. Illustreremo i concetti dietro questi importanti argomenti usando codice in due dei framework più popolari - [TensorFlow](http://Tensorflow.org) e [PyTorch](http://pytorch.org). -* **Architetture Neurali** per lavorare con immagini e testo. Copriremo modelli recenti ma potremmo essere un po' carenti rispetto allo stato dell'arte. -* Approcci di IA meno popolari, come **Algoritmi Genetici** e **Sistemi Multi-Agente**. +* Diversi approcci all'Intelligenza Artificiale, incluso il "vecchio" approccio simbolico con **Rappresentazione della Conoscenza** e ragionamento ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). +* **Reti Neurali** e **Deep Learning**, che sono al centro dell'IA moderna. Illustreremo i concetti dietro questi importanti argomenti usando codice in due dei framework più popolari - [TensorFlow](http://Tensorflow.org) e [PyTorch](http://pytorch.org). +* **Architetture Neurali** per lavorare con immagini e testo. Copriremo modelli recenti, ma potremmo essere un po' carenti sullo stato dell'arte. +* Approcci meno popolari all'IA, come **Algoritmi Genetici** e **Sistemi Multi-Agente**. -Cosa non copriremo in questo curriculum: +Cosa non tratteremo in questo curriculum: > [Trova tutte le risorse aggiuntive per questo corso nella nostra collezione Microsoft Learn](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* Casi aziendali sull'uso di **IA nel Business**. Considera di seguire il percorso formativo [Introduzione all'IA per utenti aziendali](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) su Microsoft Learn, o la [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), sviluppata in collaborazione con [INSEAD](https://www.insead.edu/). -* **Machine Learning classico**, bene descritto nel nostro [Curriculum Machine Learning per Principianti](http://github.com/Microsoft/ML-for-Beginners). -* Applicazioni pratiche di IA costruite usando i **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. Per questo, consigliamo di iniziare dai moduli Microsoft Learn su [vision](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [elaborazione del linguaggio naturale](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[Generative AI con Azure OpenAI Service](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** e altri. -* Specifici **framework cloud per ML**, come [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum), [Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum), o [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Considera l'uso dei percorsi formativi [Build and operate machine learning solutions with Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) e [Build and Operate Machine Learning Solutions with Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum). -* **IA conversazionale** e **Chat Bot**. Esiste un percorso formativo separato [Create conversational AI solutions](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum), e puoi anche fare riferimento a [questo post sul blog](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) per maggiori dettagli. -* **Matematica avanzata** dietro al deep learning. Per questo, consigliamo [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) di Ian Goodfellow, Yoshua Bengio e Aaron Courville, disponibile anche online su [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). +* Casi d'uso di **IA negli Affari**. Valuta di seguire il percorso formativo [Introduzione all'IA per utenti business](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) su Microsoft Learn, o la [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), sviluppata in collaborazione con [INSEAD](https://www.insead.edu/). +* **Machine Learning Classico**, ben descritto nel nostro [Curriculum di Machine Learning per Principianti](http://github.com/Microsoft/ML-for-Beginners). +* Applicazioni pratiche di IA costruite usando i **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. Per questo, consigliamo di iniziare con i moduli Microsoft Learn per [vision](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [elaborazione del linguaggio naturale](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[Generative AI con Azure OpenAI Service](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** e altri. +* Specifici **Framework Cloud** per ML, come [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum), [Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum), o [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Considera l'uso dei percorsi formativi [Costruire e gestire soluzioni di machine learning con Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) e [Costruire e gestire soluzioni di machine learning con Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum). +* **AI Conversazionale** e **Chat Bot**. Esiste un percorso formativo separato [Creare soluzioni AI conversazionali](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum), e puoi anche fare riferimento a [questo post del blog](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) per maggiori dettagli. +* **Matematica Profonda** dietro il deep learning. Per questo, consigliamo [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) di Ian Goodfellow, Yoshua Bengio e Aaron Courville, disponibile anche online su [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). -Per un’introduzione leggera agli argomenti di _IA nel Cloud_ potresti considerare di seguire il percorso formativo [Get started with artificial intelligence on Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum). +Per un’introduzione delicata agli argomenti _AI nel Cloud_ puoi considerare di seguire il percorso formativo [Iniziare con l'intelligenza artificiale su Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum). -# Contenuto +# Contenuti | | Link alla Lezione | PyTorch/Keras/TensorFlow | Laboratorio | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | -| 0 | [Impostazione del Corso](./lessons/0-course-setup/setup.md) | [Configura il tuo ambiente di sviluppo](./lessons/0-course-setup/how-to-run.md) | | -| I | [**Introduzione all'IA**](./lessons/1-Intro/README.md) | | | +| 0 | [Configurazione Corso](./lessons/0-course-setup/setup.md) | [Configura il tuo ambiente di sviluppo](./lessons/0-course-setup/how-to-run.md) | | +| I | [**Introduzione all’IA**](./lessons/1-Intro/README.md) | | | | 01 | [Introduzione e Storia dell'IA](./lessons/1-Intro/README.md) | - | - | | II | **IA Simbolica** | | 02 | [Rappresentazione della Conoscenza e Sistemi Esperti](./lessons/2-Symbolic/README.md) | [Sistemi Esperti](./lessons/2-Symbolic/Animals.ipynb) / [Ontologia](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Grafo Concettuale](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | | III | [**Introduzione alle Reti Neurali**](./lessons/3-NeuralNetworks/README.md) ||| -| 03 | [Perceptron](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [Notebook](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [Lab](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | -| 04 | [Perceptron Multistrato e Creazione del nostro Framework](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Notebook](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Lab](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | +| 03 | [Percettrone](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [Notebook](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [Lab](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | +| 04 | [Percettrone Multistrato e Creazione del nostro Framework](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Notebook](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Lab](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | | 05 | [Introduzione ai Framework (PyTorch/TensorFlow) e Overfitting](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [PyTorch](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [Keras](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | | IV | [**Visione Artificiale**](./lessons/4-ComputerVision/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [Esplora la Visione Artificiale su Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | | 06 | [Introduzione alla Visione Artificiale. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Notebook](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Lab](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | | 07 | [Reti Neurali Convoluzionali](./lessons/4-ComputerVision/07-ConvNets/README.md) & [Architetture CNN](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [Lab](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | -| 08 | [Reti Pre-Addestrate e Apprendimento per Trasferimento](./lessons/4-ComputerVision/08-TransferLearning/README.md) e [Trucchi per l’Addestramento](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | +| 08 | [Reti Pre-addestrate e Transfer Learning](./lessons/4-ComputerVision/08-TransferLearning/README.md) e [Trucchi per l'Addestramento](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | | 09 | [Autoencoder e VAE](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | | 10 | [Reti Generative Avversarie e Trasferimento di Stile Artistico](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | -| 11 | [Rilevamento Oggetti](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [Lab](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | +| 11 | [Rilevamento di Oggetti](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [Lab](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | | 12 | [Segmentazione Semantica. U-Net](./lessons/4-ComputerVision/12-Segmentation/README.md) | [PyTorch](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb) | | | V | [**Elaborazione del Linguaggio Naturale**](./lessons/5-NLP/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [Esplora l'Elaborazione del Linguaggio Naturale su Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| | 13 | [Rappresentazione del Testo. Bow/TF-IDF](./lessons/5-NLP/13-TextRep/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb) | | -| 14 | [Embeddings semantici delle parole. Word2Vec e GloVe](./lessons/5-NLP/14-Embeddings/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb) | | -| 15 | [Modellazione del linguaggio. Addestramento dei propri embeddings](./lessons/5-NLP/15-LanguageModeling/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [Lab](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | +| 14 | [Embedding Semantici delle Parole. Word2Vec e GloVe](./lessons/5-NLP/14-Embeddings/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb) | | +| 15 | [Modellazione del Linguaggio. Addestramento dei propri embedding](./lessons/5-NLP/15-LanguageModeling/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [Lab](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | | 16 | [Reti Neurali Ricorrenti](./lessons/5-NLP/16-RNN/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNTF.ipynb) | | | 17 | [Reti Ricorrenti Generative](./lessons/5-NLP/17-GenerativeNetworks/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb) | [Lab](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | | 18 | [Transformers. BERT.](./lessons/5-NLP/18-Transformers/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb) /[TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersTF.ipynb) | | -| 19 | [Riconoscimento Entità Nomeate](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [Lab](./lessons/5-NLP/19-NER/lab/README.md) | -| 20 | [Modelli di Linguaggio di Grande Scala, Programmazione dei Prompt e Compiti Few-Shot](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | +| 19 | [Riconoscimento di Entità Nominative](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [Lab](./lessons/5-NLP/19-NER/lab/README.md) | +| 20 | [Large Language Models, Programmazione di Prompt e Few-Shot Tasks](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | | VI | **Altre Tecniche di AI** || | | 21 | [Algoritmi Genetici](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Notebook](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | -| 22 | [Apprendimento Rinforzato Profondo](./lessons/6-Other/22-DeepRL/README.md) | [PyTorch](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[TensorFlow](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [Lab](./lessons/6-Other/22-DeepRL/lab/README.md) | +| 22 | [Deep Reinforcement Learning](./lessons/6-Other/22-DeepRL/README.md) | [PyTorch](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[TensorFlow](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [Lab](./lessons/6-Other/22-DeepRL/lab/README.md) | | 23 | [Sistemi Multi-Agente](./lessons/6-Other/23-MultiagentSystems/README.md) | | | | VII | **Etica dell'IA** | | | | 24 | [Etica dell'IA e IA Responsabile](./lessons/7-Ethics/README.md) | [Microsoft Learn: Principi di IA Responsabile](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | @@ -119,45 +118,45 @@ Per un’introduzione leggera agli argomenti di _IA nel Cloud_ potresti consider ## Ogni lezione contiene * Materiale di pre-lettura -* Jupyter Notebooks eseguibili, spesso specifici per il framework (**PyTorch** o **TensorFlow**). Il notebook eseguibile contiene anche molto materiale teorico, quindi per comprendere l'argomento è necessario seguire almeno una versione del notebook (PyTorch o TensorFlow). -* **Lab** disponibili per alcuni argomenti, che offrono l'opportunità di provare ad applicare il materiale appreso a un problema specifico. -* Alcune sezioni contengono link ai moduli di [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) che trattano argomenti correlati. +* Notebook Jupyter eseguibili, spesso specifici per il framework (**PyTorch** o **TensorFlow**). Il notebook eseguibile contiene anche molto materiale teorico, quindi per comprendere l'argomento è necessario seguire almeno una versione del notebook (PyTorch o TensorFlow). +* **Laboratori** disponibili per alcuni argomenti, che offrono l'opportunità di provare ad applicare il materiale appreso a un problema specifico. +* Alcune sezioni contengono link a moduli di [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) che trattano argomenti correlati. -## Per iniziare +## Iniziare -### 🎯 Nuovo nell'IA? Inizia Qui! +### 🎯 Nuovo all'IA? Inizia qui! -Se sei completamente nuovo nell'IA e vuoi esempi rapidi e pratici, dai un'occhiata ai nostri [**Esempi per Principianti**](./examples/README.md)! Questi includono: +Se sei completamente nuovo all'IA e vuoi esempi veloci e pratici, dai un'occhiata ai nostri [**Esempi per Principianti**](./examples/README.md)! Questi includono: - 🌟 **Hello AI World** - Il tuo primo programma di IA (riconoscimento di pattern) -- 🧠 **Rete Neurale Semplice** - Costruisci una rete neurale da zero +- 🧠 **Rete Neurale Semplice** - Crea una rete neurale da zero - 🖼️ **Classificatore di Immagini** - Classifica immagini con commenti dettagliati -- 💬 **Sentimento del Testo** - Analizza testo positivo/negativo +- 💬 **Analisi del Sentimento del Testo** - Analizza testi positivi/negativi -Questi esempi sono progettati per aiutarti a comprendere i concetti di AI prima di immergerti nel curriculum completo. +Questi esempi sono progettati per aiutarti a comprendere i concetti di AI prima di immergerti nel programma completo. -### 📚 Configurazione del Curriculum Completo +### 📚 Configurazione del Programma Completo -- Abbiamo creato una [lezione di configurazione](./lessons/0-course-setup/setup.md) per aiutarti con l'impostazione del tuo ambiente di sviluppo. - Per gli educatori, abbiamo creato anche una [lezione di configurazione del curriculum](./lessons/0-course-setup/for-teachers.md)! -- Come [Eseguire il codice in VSCode o Codepace](./lessons/0-course-setup/how-to-run.md) +- Abbiamo creato una [lezione di configurazione](./lessons/0-course-setup/setup.md) per aiutarti a configurare il tuo ambiente di sviluppo. - Per gli insegnanti, abbiamo creato anche una [lezione di configurazione del programma](./lessons/0-course-setup/for-teachers.md)! +- Come [eseguire il codice in VSCode o in un Codespace](./lessons/0-course-setup/how-to-run.md) Segui questi passaggi: -Fork del Repository: Clicca sul pulsante "Fork" in alto a destra di questa pagina. +Fork del Repository: Clicca sul pulsante "Fork" nell'angolo in alto a destra di questa pagina. Clona il Repository: `git clone https://github.com/microsoft/AI-For-Beginners.git` -Non dimenticare di mettere una stella (🌟) a questo repository per trovarlo più facilmente in seguito. +Non dimenticare di mettere una stella (🌟) a questo repo per trovarlo più facilmente in seguito. ## Incontra altri Studenti -Unisciti al nostro [server Discord ufficiale sull'AI](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) per incontrare e fare rete con altri studenti che seguono questo corso e ricevere supporto. +Unisciti al nostro [server Discord ufficiale per AI](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) per incontrare e fare rete con altri studenti che seguono questo corso e ricevere supporto. -Se hai feedback sul prodotto o domande durante lo sviluppo visita il nostro [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) +Se hai commenti sul prodotto o domande mentre costruisci visita il nostro [Forum degli Sviluppatori Azure AI Foundry](https://aka.ms/foundry/forum) -## Quiz +## Quiz -> **Una nota sui quiz**: Tutti i quiz sono contenuti nella cartella Quiz-app in etc\quiz-app, o [Online Qui](https://ff-quizzes.netlify.app/) Sono collegati dalle lezioni, l'app del quiz può essere eseguita localmente o distribuita su Azure; segui le istruzioni nella cartella `quiz-app`. Stanno gradualmente venendo localizzati. +> **Una nota sui quiz**: Tutti i quiz si trovano nella cartella Quiz-app in etc\quiz-app, o [Online Qui](https://ff-quizzes.netlify.app/) Sono collegati dalle lezioni; l'app dei quiz può essere eseguita localmente o distribuita su Azure; segui le istruzioni nella cartella `quiz-app`. Sono progressivamente localizzati. ## Aiuto Richiesto @@ -166,68 +165,68 @@ Hai suggerimenti o hai trovato errori di ortografia o di codice? Apri un issue o ## Ringraziamenti Speciali * **✍️ Autore Principale:** [Dmitry Soshnikov](http://soshnikov.com), PhD -* **🔥 Editore:** [Jen Looper](https://twitter.com/jenlooper), PhD -* **🎨 Illustratrice Sketchnote:** [Tomomi Imura](https://twitter.com/girlie_mac) +* **🔥 Editor:** [Jen Looper](https://twitter.com/jenlooper), PhD +* **🎨 Illustratore di Sketchnote:** [Tomomi Imura](https://twitter.com/girlie_mac) * **✅ Creatrice dei Quiz:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) -* **🙏 Contributor Principali:** [Evgenii Pishchik](https://github.com/Pe4enIks) +* **🙏 Collaboratori Principali:** [Evgenii Pishchik](https://github.com/Pe4enIks) -## Altri Curricula +## Altri Programmi -Il nostro team produce altri curricula! Dai un’occhiata a: +Il nostro team produce altri programmi! Dai un'occhiata a: ### LangChain -[![LangChain4j for Beginners](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners) -[![LangChain.js for Beginners](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin) +[![LangChain4j per Principianti](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners) +[![LangChain.js per Principianti](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin) --- -### Azure / Edge / MCP / Agents -[![AZD for Beginners](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst) -[![Edge AI for Beginners](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst) -[![MCP for Beginners](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst) -[![AI Agents for Beginners](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst) +### Azure / Edge / MCP / Agenti +[![AZD per Principianti](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst) +[![Edge AI per Principianti](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst) +[![MCP per Principianti](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst) +[![Agenti AI per Principianti](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst) --- -### Serie su AI Generativa -[![Generative AI for Beginners](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst) -[![Generative AI (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst) -[![Generative AI (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst) -[![Generative AI (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst) +### Serie AI Generativa +[![AI Generativa per Principianti](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst) +[![AI Generativa (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst) +[![AI Generativa (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst) +[![AI Generativa (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst) --- -### Apprendimento Base -[![ML for Beginners](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) -[![Data Science for Beginners](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst) -[![AI for Beginners](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst) -[![Cybersecurity for Beginners](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung) -[![Web Dev for Beginners](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst) -[![IoT for Beginners](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst) -[![XR Development for Beginners](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst) +### Apprendimento Core +[![ML per Principianti](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) +[![Data Science per Principianti](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst) +[![AI per Principianti](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst) +[![Cybersecurity per Principianti](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung) +[![Sviluppo Web per Principianti](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst) +[![IoT per Principianti](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst) +[![Sviluppo XR per Principianti](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst) --- ### Serie Copilot -[![Copilot for AI Paired Programming](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) -[![Copilot for C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst) -[![Copilot Adventure](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst) +[![Copilot per Programmazione Accoppiata AI](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) +[![Copilot per C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst) +[![Avventure con Copilot](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst) -## Ricevere Aiuto +## Ottenere Aiuto -Se rimani bloccato o hai domande sulla creazione di app AI. Unisciti a studenti e sviluppatori esperti nelle discussioni su MCP. È una comunità di supporto dove le domande sono benvenute e la conoscenza viene condivisa liberamente. +Se ti blocchi o hai domande sulla creazione di app AI. Unisciti ad altri studenti e sviluppatori esperti nelle discussioni su MCP. È una comunità di supporto dove le domande sono benvenute e la conoscenza viene condivisa liberamente. [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -Se hai feedback sul prodotto o errori durante lo sviluppo visita: +Se hai feedback sul prodotto o errori durante la creazione visita: [![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) --- -**Disclaimer**: -Questo documento è stato tradotto utilizzando il servizio di traduzione automatica AI [Co-op Translator](https://github.com/Azure/co-op-translator). Pur impegnandoci per garantire l’accuratezza, si prega di considerare che le traduzioni automatiche possono contenere errori o imprecisioni. Il documento originale nella sua lingua originale deve essere considerato la fonte autorevole. Per informazioni critiche, si raccomanda una traduzione professionale effettuata da un traduttore umano. Non ci assumiamo alcuna responsabilità per eventuali malintesi o interpretazioni errate derivanti dall’uso di questa traduzione. +**Dichiarazione di responsabilità**: +Questo documento è stato tradotto utilizzando il servizio di traduzione automatica [Co-op Translator](https://github.com/Azure/co-op-translator). Pur impegnandoci per garantire l’accuratezza, si prega di tenere presente che le traduzioni automatiche possono contenere errori o imprecisioni. Il documento originale nella sua lingua nativa deve essere considerato la fonte autorevole. Per informazioni critiche, si raccomanda una traduzione professionale effettuata da un traduttore qualificato. Non ci assumiamo alcuna responsabilità per eventuali incomprensioni o interpretazioni errate derivanti dall’uso di questa traduzione. \ No newline at end of file diff --git a/translations/it/lessons/0-course-setup/how-to-run.md b/translations/it/lessons/0-course-setup/how-to-run.md index f7c385a7..1a5da3d7 100644 --- a/translations/it/lessons/0-course-setup/how-to-run.md +++ b/translations/it/lessons/0-course-setup/how-to-run.md @@ -1,21 +1,21 @@ -# Come Eseguire il Codice +# Come eseguire il codice -Questo curriculum contiene molti esempi eseguibili e laboratori che vorresti provare. Per farlo, hai bisogno della possibilità di eseguire codice Python nei Jupyter Notebook forniti come parte di questo curriculum. Hai diverse opzioni per eseguire il codice: +Questo curriculum contiene molti esempi eseguibili e laboratori che vorresti eseguire. Per farlo, hai bisogno della capacità di eseguire codice Python nei Jupyter Notebook forniti come parte di questo curriculum. Hai diverse opzioni per eseguire il codice: -## Eseguire localmente sul tuo computer +## Esegui localmente sul tuo computer -Per eseguire il codice localmente sul tuo computer, devi avere una qualche versione di Python installata. Personalmente consiglio di installare **[miniconda](https://conda.io/en/latest/miniconda.html)** - è un'installazione piuttosto leggera che supporta il gestore di pacchetti `conda` per diversi **ambienti virtuali** Python. +Per eseguire il codice localmente sul tuo computer, è necessaria un'installazione di Python. Una raccomandazione è installare **[miniconda](https://conda.io/en/latest/miniconda.html)** - si tratta di un'installazione piuttosto leggera che supporta il gestore di pacchetti `conda` per diversi **ambienti virtuali** Python. -Dopo aver installato miniconda, devi clonare il repository e creare un ambiente virtuale da utilizzare per questo corso: +Dopo aver installato miniconda, clona il repository e crea un ambiente virtuale da utilizzare per questo corso: ```bash git clone http://github.com/microsoft/ai-for-beginners @@ -24,54 +24,57 @@ conda env create --name ai4beg --file .devcontainer/environment.yml conda activate ai4beg ``` -### Utilizzare Visual Studio Code con l'Estensione Python +### Usare Visual Studio Code con l'estensione Python -Probabilmente il modo migliore per utilizzare il curriculum è aprirlo in [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) con l'[Estensione Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste). +Questo curriculum è meglio utilizzato aprendolo in [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) con l'[estensione Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste). -> **Note**: Una volta clonato e aperto il directory in VS Code, ti verrà automaticamente suggerito di installare le estensioni Python. Dovrai anche installare miniconda come descritto sopra. +> **Nota**: Una volta che cloni e apri la directory in VS Code, ti suggerirà automaticamente di installare le estensioni Python. Dovrai anche installare miniconda come descritto sopra. -> **Note**: Se VS Code ti suggerisce di riaprire il repository in un container, devi rifiutare per utilizzare l'installazione locale di Python. +> **Nota**: Se VS Code ti suggerisce di riaprire il repository in un container, dovresti rifiutare per usare l'installazione Python locale. -### Utilizzare Jupyter nel Browser +### Usare Jupyter nel browser -Puoi anche utilizzare l'ambiente Jupyter direttamente dal browser sul tuo computer. In realtà, sia Jupyter classico che Jupyter Hub offrono un ambiente di sviluppo piuttosto comodo con completamento automatico, evidenziazione del codice, ecc. +Puoi anche usare un ambiente Jupyter dal browser sul tuo computer. Sia il classico Jupyter che JupyterHub offrono un ambiente di sviluppo comodo con completamento automatico, evidenziazione del codice, ecc. -Per avviare Jupyter localmente, vai nella directory del corso ed esegui: +Per iniziare Jupyter localmente, vai nella directory del corso ed esegui: ```bash jupyter notebook ``` -oppure +o ```bash jupyterhub ``` -Puoi quindi navigare tra i file `.ipynb`, aprirli e iniziare a lavorare. +Puoi quindi navigare in uno qualsiasi dei file `.ipynb`, aprirli e iniziare a lavorare. -### Eseguire in un container +### Esecuzione in contenitore -Un'alternativa all'installazione di Python sarebbe eseguire il codice in un container. Poiché il nostro repository contiene una cartella speciale `.devcontainer` che istruisce su come costruire un container per questo repo, VS Code ti offrirà di riaprire il codice in un container. Questo richiederà l'installazione di Docker e sarà anche più complesso, quindi lo consigliamo agli utenti più esperti. +Un'alternativa all'installazione di Python sarebbe eseguire il codice in un contenitore. Poiché il nostro repository fornisce una cartella speciale `.devcontainer` che spiega come costruire un contenitore per questo repo, VS Code offre l'opportunità di riaprire il codice in un contenitore. Questo richiederà l'installazione di Docker e sarà anche più complesso, quindi lo consigliamo a utenti più esperti. -## Eseguire nel Cloud +## Esecuzione nel cloud -Se non vuoi installare Python localmente e hai accesso a risorse cloud, una buona alternativa sarebbe eseguire il codice nel cloud. Ci sono diversi modi per farlo: +Se non vuoi installare Python localmente e hai accesso ad alcune risorse cloud, una buona alternativa è eseguire il codice nel cloud. Ci sono diversi modi per farlo: -* Utilizzando **[GitHub Codespaces](https://github.com/features/codespaces)**, che è un ambiente virtuale creato per te su GitHub, accessibile tramite l'interfaccia browser di VS Code. Se hai accesso a Codespaces, puoi semplicemente cliccare sul pulsante **Code** nel repository, avviare un codespace e iniziare rapidamente. +* Usare **[GitHub Codespaces](https://github.com/features/codespaces)**, che è un ambiente virtuale creato per te su GitHub, accessibile tramite un'interfaccia browser di VS Code. Se hai accesso a Codespaces, puoi semplicemente cliccare sul pulsante **Code** nel repo, avviare un codespace e iniziare rapidamente. +* Usare **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. [Binder](https://mybinder.org) offre risorse di calcolo gratuite fornite nel cloud per persone come te per testare del codice su GitHub. C'è un pulsante nella pagina principale per aprire il repository in Binder - questo ti porterà rapidamente al sito di binder, che costruirà un contenitore sottostante e avvierà un'interfaccia web Jupyter per te senza interruzioni. -* Utilizzando **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. [Binder](https://mybinder.org) offre risorse di calcolo gratuite nel cloud per persone come te che vogliono testare del codice su GitHub. C'è un pulsante nella pagina principale per aprire il repository in Binder - questo ti porterà rapidamente al sito di Binder, che costruirà il container sottostante e avvierà l'interfaccia web di Jupyter per te senza problemi. +> **Nota**: Per prevenire abusi, Binder ha accesso bloccato ad alcune risorse web. Questo potrebbe impedire che parte del codice funzioni, specialmente quello che cerca di scaricare modelli e/o dataset da Internet pubblico. Potresti dover trovare alcune soluzioni alternative. Inoltre, le risorse di calcolo fornite da Binder sono piuttosto basilari, quindi l'addestramento sarà lento, specialmente nelle lezioni successive, più complesse. -> **Note**: Per prevenire abusi, Binder ha accesso a alcune risorse web bloccato. Questo potrebbe impedire il funzionamento di parte del codice che scarica modelli e/o dataset da Internet pubblico. Potresti dover trovare delle soluzioni alternative. Inoltre, le risorse di calcolo fornite da Binder sono piuttosto basilari, quindi l'addestramento sarà lento, specialmente nelle lezioni più complesse. +## Esecuzione nel cloud con GPU -## Eseguire nel Cloud con GPU +Alcune lezioni successive di questo curriculum trarrebbero grande beneficio dal supporto GPU. L'addestramento dei modelli, per esempio, può essere dolorosamente lento altrimenti. Ci sono alcune opzioni che puoi seguire, specialmente se hai accesso al cloud tramite [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste), o tramite la tua istituzione: -Alcune delle lezioni più avanzate di questo curriculum trarrebbero grande beneficio dal supporto GPU, perché altrimenti l'addestramento sarebbe estremamente lento. Ci sono alcune opzioni che puoi seguire, specialmente se hai accesso al cloud tramite [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) o tramite la tua istituzione: +* Crea una [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) e connettiti tramite Jupyter. Puoi quindi clonare il repo direttamente sulla macchina e iniziare a imparare. Le VM della serie NC hanno supporto GPU. -* Crea una [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) e connettiti ad essa tramite Jupyter. Puoi quindi clonare il repository direttamente sulla macchina e iniziare a imparare. Le VM della serie NC hanno supporto GPU. +> **Nota**: Alcuni abbonamenti, inclusi Azure for Students, non forniscono supporto GPU di default. Potresti dover richiedere core GPU aggiuntivi tramite richiesta di supporto tecnico. -> **Note**: Alcuni abbonamenti, inclusi Azure for Students, non forniscono supporto GPU di default. Potresti dover richiedere core GPU aggiuntivi tramite una richiesta di supporto tecnico. +* Crea un [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) e quindi usa la funzione Notebook lì. [Questo video](https://azure-for-academics.github.io/quickstart/azureml-papers/) mostra come clonare un repository in un notebook Azure ML e iniziare a usarlo. -* Crea un [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) e utilizza la funzione Notebook lì. [Questo video](https://azure-for-academics.github.io/quickstart/azureml-papers/) mostra come clonare un repository in un notebook di Azure ML e iniziare a usarlo. +Puoi anche usare Google Colab, che viene fornito con un certo supporto GPU gratuito, e caricare lì i Jupyter Notebook per eseguirli uno alla volta. -Puoi anche utilizzare Google Colab, che offre un certo supporto GPU gratuito, e caricare i Jupyter Notebook lì per eseguirli uno per uno. +--- -**Disclaimer**: -Questo documento è stato tradotto utilizzando il servizio di traduzione automatica [Co-op Translator](https://github.com/Azure/co-op-translator). Sebbene ci impegniamo per garantire l'accuratezza, si prega di notare che le traduzioni automatiche possono contenere errori o imprecisioni. Il documento originale nella sua lingua nativa dovrebbe essere considerato la fonte autorevole. Per informazioni critiche, si raccomanda una traduzione professionale effettuata da un traduttore umano. Non siamo responsabili per eventuali fraintendimenti o interpretazioni errate derivanti dall'uso di questa traduzione. \ No newline at end of file + +**Disclaimer**: +Questo documento è stato tradotto utilizzando il servizio di traduzione AI [Co-op Translator](https://github.com/Azure/co-op-translator). Pur impegnandoci per garantire accuratezza, si prega di considerare che le traduzioni automatiche possono contenere errori o inesattezze. Il documento originale nella sua lingua madre deve essere considerato la fonte autorevole. Per informazioni importanti, si consiglia una traduzione professionale effettuata da un esperto umano. Non ci assumiamo responsabilità per eventuali malintesi o interpretazioni errate derivanti dall’uso di questa traduzione. + \ No newline at end of file diff --git a/translations/it/lessons/2-Symbolic/Animals.ipynb b/translations/it/lessons/2-Symbolic/Animals.ipynb index 1eedbc4e..b7f357c1 100644 --- a/translations/it/lessons/2-Symbolic/Animals.ipynb +++ b/translations/it/lessons/2-Symbolic/Animals.ipynb @@ -6,25 +6,25 @@ "collapsed": true }, "source": [ - "# Implementazione di un Sistema Esperto sugli Animali\n", + "# Implementazione di un Sistema Esperto per Animali\n", "\n", - "Un esempio tratto da [AI for Beginners Curriculum](http://github.com/microsoft/ai-for-beginners).\n", + "Un esempio dal [Curriculum AI per Principianti](http://github.com/microsoft/ai-for-beginners).\n", "\n", - "In questo esempio, implementeremo un semplice sistema basato sulla conoscenza per determinare un animale in base ad alcune caratteristiche fisiche. Il sistema può essere rappresentato dal seguente albero AND-OR (questa è solo una parte dell'intero albero, possiamo facilmente aggiungere altre regole):\n", + "In questo esempio, implementeremo un semplice sistema basato sulla conoscenza per determinare un animale in base ad alcune caratteristiche fisiche. Il sistema può essere rappresentato dal seguente albero AND-OR (questa è una parte dell'intero albero, possiamo facilmente aggiungere altre regole):\n", "\n", - "![](../../../../translated_images/it/AND-OR-Tree.5592d2c70187f283.webp)\n" + "![](../../../../../../translated_images/it/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Il nostro shell di sistemi esperti con inferenza retrograda\n", + "## Il nostro shell per sistemi esperti con inferenza backward\n", "\n", - "Proviamo a definire un linguaggio semplice per la rappresentazione della conoscenza basato su regole di produzione. Utilizzeremo classi Python come parole chiave per definire le regole. Ci saranno essenzialmente 3 tipi di classi:\n", - "* `Ask` rappresenta una domanda che deve essere posta all'utente. Contiene il set di risposte possibili.\n", - "* `If` rappresenta una regola ed è solo una sintassi semplificata per memorizzare il contenuto della regola.\n", - "* `AND`/`OR` sono classi per rappresentare i rami AND/OR dell'albero. Memorizzano semplicemente la lista degli argomenti al loro interno. Per semplificare il codice, tutta la funzionalità è definita nella classe genitore `Content`.\n" + "Proviamo a definire un linguaggio semplice per la rappresentazione della conoscenza basato su regole di produzione. Useremo le classi Python come parole chiave per definire le regole. Ci sarebbero essenzialmente 3 tipi di classi:\n", + "* `Ask` rappresenta una domanda che deve essere posta all'utente. Contiene l'insieme delle possibili risposte.\n", + "* `If` rappresenta una regola, ed è solo uno zucchero sintattico per memorizzare il contenuto della regola\n", + "* `AND`/`OR` sono classi per rappresentare rami AND/OR dell'albero. Memorizzano semplicemente la lista degli argomenti al loro interno. Per semplificare il codice, tutta la funzionalità è definita nella classe padre `Content`\n" ] }, { @@ -99,13 +99,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Per eseguire l'inferenza inversa, definiremo la classe `Knowledgebase`. Essa conterrà:\n", - "* `Memoria` di lavoro - un dizionario che associa attributi a valori\n", - "* `Regole` della Knowledgebase nel formato definito sopra\n", + "Per eseguire l'inferenza all'indietro, definiremo la classe `Knowledgebase`. Essa conterrà:\n", + "* Una `memory` di lavoro - un dizionario che mappa attributi a valori\n", + "* Le `rules` della knowledgebase nel formato definito sopra\n", "\n", - "I due metodi principali sono:\n", - "* `get` per ottenere il valore di un attributo, eseguendo l'inferenza se necessario. Ad esempio, `get('color')` recupererà il valore di uno slot colore (chiederà se necessario e memorizzerà il valore per un utilizzo successivo nella memoria di lavoro). Se chiediamo `get('color:blue')`, chiederà un colore e poi restituirà un valore `y`/`n` a seconda del colore.\n", - "* `eval` esegue l'inferenza vera e propria, ovvero attraversa l'albero AND/OR, valuta sotto-obiettivi, ecc.\n" + "Due metodi principali sono:\n", + "* `get` per ottenere il valore di un attributo, eseguendo l'inferenza se necessario. Ad esempio, `get('color')` otterrà il valore di uno slot colore (chiederà se necessario, e memorizzerà il valore per uso futuro nella working memory). Se chiediamo `get('color:blue')`, chiederà un colore, e quindi restituirà il valore `y`/`n` a seconda del colore.\n", + "* `eval` esegue la vera inferenza, cioè attraversa l'albero AND/OR, valuta i sotto-obiettivi, ecc.\n" ] }, { @@ -172,7 +172,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Ora definiamo la nostra base di conoscenza sugli animali e svolgiamo la consultazione. Nota che questa chiamata ti farà delle domande. Puoi rispondere digitando `s`/`n` per le domande sì-no, oppure specificando un numero (0..N) per le domande con risposte a scelta multipla più lunghe.\n" + "Definiamo ora la nostra base di conoscenza sugli animali ed eseguiamo la consultazione. Nota che questa chiamata ti farà delle domande. Puoi rispondere digitando `y`/`n` per domande sì-no, oppure specificando un numero (0..N) per domande con risposte multiple più lunghe.\n" ] }, { @@ -229,11 +229,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Utilizzo di PyKnow per l'Inferenza in Avanti\n", + "## Utilizzo di Experta per l'Inferenza Diretta\n", "\n", - "Nel prossimo esempio, cercheremo di implementare l'inferenza in avanti utilizzando una delle librerie per la rappresentazione della conoscenza, [PyKnow](https://github.com/buguroo/pyknow/). **PyKnow** è una libreria per creare sistemi di inferenza in avanti in Python, progettata per essere simile al classico vecchio sistema [CLIPS](http://www.clipsrules.net/index.html).\n", + "Nel prossimo esempio, cercheremo di implementare l'inferenza diretta utilizzando una delle librerie per la rappresentazione della conoscenza, [Experta](https://github.com/nilp0inter/experta). **Experta** è una libreria per creare sistemi di inferenza diretta in Python, progettata per essere simile al classico vecchio sistema [CLIPS](http://www.clipsrules.net/index.html).\n", "\n", - "Avremmo potuto anche implementare il chaining in avanti da soli senza troppi problemi, ma le implementazioni ingenue di solito non sono molto efficienti. Per un confronto delle regole più efficace, viene utilizzato un algoritmo speciale chiamato [Rete](https://en.wikipedia.org/wiki/Rete_algorithm).\n" + "Avremmo potuto anche implementare la catena diretta da soli senza molti problemi, ma le implementazioni ingenue di solito non sono molto efficienti. Per un matching delle regole più efficace si utilizza un algoritmo speciale chiamato [Rete](https://en.wikipedia.org/wiki/Rete_algorithm).\n" ] }, { @@ -247,32 +247,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "Collecting git+https://github.com/buguroo/pyknow/\n", - " Cloning https://github.com/buguroo/pyknow/ to /tmp/pip-req-build-3cqeulyl\n", - " Running command git clone --filter=blob:none --quiet https://github.com/buguroo/pyknow/ /tmp/pip-req-build-3cqeulyl\n", - " Resolved https://github.com/buguroo/pyknow/ to commit 48818336f2e9a126f1964f2d8dc22d37ff800fe8\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting frozendict==1.2\n", - " Using cached frozendict-1.2.tar.gz (2.6 kB)\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting schema==0.6.7\n", - " Using cached schema-0.6.7-py2.py3-none-any.whl (14 kB)\n", - "Building wheels for collected packages: pyknow, frozendict\n", - " Building wheel for pyknow (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for pyknow: filename=pyknow-1.7.0-py3-none-any.whl size=34228 sha256=b7de5b09292c4007667c72f69b98d5a1b5f7324ff15f9dd8e077c3d5f7aade42\n", - " Stored in directory: /tmp/pip-ephem-wheel-cache-k7jpave7/wheels/81/1a/d3/f6c15dbe1955598a37755215f2a10449e7418500d7bd4b9508\n", - " Building wheel for frozendict (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for frozendict: filename=frozendict-1.2-py3-none-any.whl size=3148 sha256=2863d55c240d2409cddf05ccfe600591f8478681549fc97555c47c90dc6bb160\n", - " Stored in directory: /home/rg/.cache/pip/wheels/49/ac/f8/cb8120244e710bdb479c86198b03c7b08c3c2d3d2bf448fd6e\n", - "Successfully built pyknow frozendict\n", - "Installing collected packages: schema, frozendict, pyknow\n", - "Successfully installed frozendict-1.2 pyknow-1.7.0 schema-0.6.7\n" + "Collecting git+https://github.com/nilp0inter/experta\n", + " Cloning https://github.com/nilp0inter/experta to /tmp/pip-req-build-7qurtwk3\n", + " Running command git clone --filter=blob:none --quiet https://github.com/nilp0inter/experta /tmp/pip-req-build-7qurtwk3\n", + " Resolved https://github.com/nilp0inter/experta to commit c6d5834b123861f5ae09e7d07027dc98bec58741\n", + " Installing build dependencies ... \u001b[?25ldone\n", + "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", + "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25hRequirement already satisfied: frozendict~=2.4.6 in /opt/conda/envs/ai4beg/lib/python3.12/site-packages (from experta==1.9.5.dev1) (2.4.7)\n", + "Collecting schema~=0.6.7 (from experta==1.9.5.dev1)\n", + " Downloading schema-0.6.8-py2.py3-none-any.whl.metadata (14 kB)\n", + "Downloading schema-0.6.8-py2.py3-none-any.whl (14 kB)\n", + "Building wheels for collected packages: experta\n", + " Building wheel for experta (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25h Created wheel for experta: filename=experta-1.9.5.dev1-py3-none-any.whl size=34804 sha256=888c459512a5e713f4b674caa9a0f96cfdf07ec0d6eb56cc318ce0653d218014\n", + " Stored in directory: /tmp/pip-ephem-wheel-cache-1eeii9zy/wheels/3d/e8/bb/22d7956359603fa8dd679aa09f5b8efb3f29991c3986fdc787\n", + "Successfully built experta\n", + "Installing collected packages: schema, experta\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2/2\u001b[0m [experta]\n", + "\u001b[1A\u001b[2KSuccessfully installed experta-1.9.5.dev1 schema-0.6.8\n" ] } ], "source": [ "import sys\n", - "!{sys.executable} -m pip install git+https://github.com/buguroo/pyknow/" + "!{sys.executable} -m pip install git+https://github.com/nilp0inter/experta" ] }, { @@ -283,15 +282,15 @@ }, "outputs": [], "source": [ - "from pyknow import *\n", - "#import pyknow" + "from experta import *\n", + "#import experta" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Definiremo il nostro sistema come una classe che sottoclassa `KnowledgeEngine`. Ogni regola è definita da una funzione separata con l'annotazione `@Rule`, che specifica quando la regola deve essere attivata. All'interno della regola, possiamo aggiungere nuovi fatti utilizzando la funzione `declare`, e l'aggiunta di questi fatti comporterà l'attivazione di altre regole da parte del motore di inferenza in avanti.\n" + "Definiremo il nostro sistema come una classe che estende `KnowledgeEngine`. Ogni regola è definita da una funzione separata con annotazione `@Rule`, che specifica quando la regola deve essere attivata. All'interno della regola, possiamo aggiungere nuovi fatti usando la funzione `declare`, e l'aggiunta di questi fatti comporterà l'attivazione di altre regole da parte del motore di inferenza in avanti.\n" ] }, { @@ -378,7 +377,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Una volta definito un knowledgebase, popoliamo la nostra memoria di lavoro con alcuni fatti iniziali e poi chiamiamo il metodo `run()` per eseguire l'inferenza. Come risultato, puoi vedere che nuovi fatti dedotti vengono aggiunti alla memoria di lavoro, incluso il fatto finale sull'animale (se abbiamo impostato correttamente tutti i fatti iniziali).\n" + "Una volta definita una base di conoscenza, popoliamo la nostra memoria di lavoro con alcuni fatti iniziali, e poi chiamiamo il metodo `run()` per eseguire l'inferenza. Puoi vedere come risultato che nuovi fatti dedotti vengono aggiunti alla memoria di lavoro, incluso il fatto finale sull'animale (se abbiamo impostato correttamente tutti i fatti iniziali).\n" ] }, { @@ -440,7 +439,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n---\n\n**Disclaimer**: \nQuesto documento è stato tradotto utilizzando il servizio di traduzione automatica [Co-op Translator](https://github.com/Azure/co-op-translator). Sebbene ci impegniamo per garantire l'accuratezza, si prega di notare che le traduzioni automatiche possono contenere errori o imprecisioni. Il documento originale nella sua lingua nativa dovrebbe essere considerato la fonte autorevole. Per informazioni critiche, si raccomanda una traduzione professionale effettuata da un traduttore umano. Non siamo responsabili per eventuali incomprensioni o interpretazioni errate derivanti dall'uso di questa traduzione.\n" + "---\n\n\n**Dichiarazione di non responsabilità**:\nQuesto documento è stato tradotto utilizzando il servizio di traduzione automatica AI [Co-op Translator](https://github.com/Azure/co-op-translator). Pur impegnandoci per l'accuratezza, si prega di notare che le traduzioni automatiche possono contenere errori o imprecisioni. Il documento originale nella sua lingua nativa deve essere considerato la fonte autorevole. Per informazioni critiche, è consigliata una traduzione professionale effettuata da un umano. Non ci assumiamo alcuna responsabilità per incomprensioni o interpretazioni errate derivanti dall'uso di questa traduzione.\n\n" ] } ], @@ -467,8 +466,8 @@ "version": "3.11.2" }, "coopTranslator": { - "original_hash": "ab2bd97b0453415b89a469284609a8ce", - "translation_date": "2025-08-28T13:35:06+00:00", + "original_hash": "8ef43db4b9182239fd150a76bd494fdb", + "translation_date": "2026-01-15T14:04:17+00:00", "source_file": "lessons/2-Symbolic/Animals.ipynb", "language_code": "it" } diff --git a/translations/it/lessons/2-Symbolic/README.md b/translations/it/lessons/2-Symbolic/README.md index 02df9204..5774742b 100644 --- a/translations/it/lessons/2-Symbolic/README.md +++ b/translations/it/lessons/2-Symbolic/README.md @@ -1,116 +1,116 @@ # Rappresentazione della Conoscenza e Sistemi Esperti -![Riepilogo del contenuto sull'IA simbolica](../../../../translated_images/it/ai-symbolic.715a30cb610411a6.webp) +![Riassunto del contenuto di AI simbolica](../../../../../../translated_images/it/ai-symbolic.715a30cb610411a6.webp) > Sketchnote di [Tomomi Imura](https://twitter.com/girlie_mac) -La ricerca sull'intelligenza artificiale si basa sulla ricerca della conoscenza, per comprendere il mondo in modo simile agli esseri umani. Ma come si può fare? +La ricerca dell'intelligenza artificiale si basa su una ricerca della conoscenza, per comprendere il mondo in modo simile a come fanno gli esseri umani. Ma come si può fare questo? ## [Quiz pre-lezione](https://ff-quizzes.netlify.app/en/ai/quiz/3) -Nei primi giorni dell'IA, l'approccio top-down per creare sistemi intelligenti (discusso nella lezione precedente) era molto popolare. L'idea era di estrarre la conoscenza dalle persone in una forma leggibile dalle macchine e usarla per risolvere automaticamente i problemi. Questo approccio si basava su due grandi concetti: +Nei primi giorni dell'IA, l'approccio top-down alla creazione di sistemi intelligenti (discusso nella lezione precedente) era popolare. L'idea era di estrarre la conoscenza dalle persone in una qualche forma interpretabile dalla macchina, e poi usarla per risolvere problemi automaticamente. Questo approccio si basava su due grandi idee: * Rappresentazione della Conoscenza * Ragionamento ## Rappresentazione della Conoscenza -Uno dei concetti importanti nell'IA simbolica è la **conoscenza**. È importante distinguere la conoscenza da *informazione* o *dati*. Ad esempio, si può dire che i libri contengono conoscenza, perché si possono studiare e diventare esperti. Tuttavia, ciò che i libri contengono è in realtà chiamato *dati*, e leggendo i libri e integrando questi dati nel nostro modello del mondo, li convertiamo in conoscenza. +Uno dei concetti importanti nell'IA simbolica è la **conoscenza**. È importante differenziare la conoscenza da *informazione* o *dati*. Per esempio, si può dire che i libri contengono conoscenza, perché studiando i libri si può diventare esperti. Tuttavia, ciò che i libri contengono in realtà si chiama *dati*, e leggendo i libri e integrando questi dati nel nostro modello del mondo trasformiamo tali dati in conoscenza. -> ✅ **Conoscenza** è ciò che è contenuto nella nostra mente e rappresenta la nostra comprensione del mondo. Si ottiene attraverso un processo attivo di **apprendimento**, che integra le informazioni ricevute nel nostro modello attivo del mondo. +> ✅ **Conoscenza** è qualcosa che è contenuta nella nostra testa e rappresenta la nostra comprensione del mondo. Viene ottenuta tramite un processo attivo di **apprendimento**, che integra pezzi di informazione che riceviamo nel nostro modello attivo del mondo. -Spesso non definiamo rigorosamente la conoscenza, ma la allineiamo ad altri concetti correlati utilizzando la [Piramide DIKW](https://en.wikipedia.org/wiki/DIKW_pyramid). Essa contiene i seguenti concetti: +Più spesso, non definiamo la conoscenza in modo rigoroso, ma la allineiamo con altri concetti correlati usando la [Piràmide DIKW](https://it.wikipedia.org/wiki/Piramide_DI_KW). Contiene i seguenti concetti: -* **Dati** sono rappresentati su supporti fisici, come testo scritto o parole pronunciate. I dati esistono indipendentemente dagli esseri umani e possono essere trasmessi tra persone. -* **Informazione** è il modo in cui interpretiamo i dati nella nostra mente. Ad esempio, quando sentiamo la parola *computer*, abbiamo una certa comprensione di cosa sia. -* **Conoscenza** è l'informazione integrata nel nostro modello del mondo. Ad esempio, una volta che impariamo cosa sia un computer, iniziamo ad avere idee su come funziona, quanto costa e a cosa può servire. Questa rete di concetti interrelati forma la nostra conoscenza. -* **Saggezza** è un ulteriore livello di comprensione del mondo e rappresenta una sorta di *meta-conoscenza*, ad esempio una nozione su come e quando utilizzare la conoscenza. +* **Dati** sono qualcosa rappresentato in un supporto fisico, come testo scritto o parole pronunciate. I dati esistono indipendentemente dagli esseri umani e possono essere trasmessi tra persone. +* **Informazione** è come interpretiamo i dati nella nostra mente. Per esempio, quando sentiamo la parola *computer*, abbiamo una certa comprensione di cosa sia. +* **Conoscenza** è l'informazione integrata nel nostro modello del mondo. Per esempio, una volta che impariamo cosa è un computer, iniziamo ad avere alcune idee su come funziona, quanto costa e a cosa può essere usato. Questa rete di concetti interrelati forma la nostra conoscenza. +* **Saggezza** è un livello ulteriore della nostra comprensione del mondo, e rappresenta la *meta-conoscenza*, es. una nozione su come e quando la conoscenza dovrebbe essere usata. - + -*Immagine [da Wikipedia](https://commons.wikimedia.org/w/index.php?curid=37705247), di Longlivetheux - Opera propria, CC BY-SA 4.0* +*Immagine [da Wikipedia](https://commons.wikimedia.org/w/index.php?curid=37705247), Di Longlivetheux - Opera propria, CC BY-SA 4.0* -Pertanto, il problema della **rappresentazione della conoscenza** è trovare un modo efficace per rappresentare la conoscenza all'interno di un computer sotto forma di dati, per renderla automaticamente utilizzabile. Questo può essere visto come uno spettro: +Quindi, il problema della **rappresentazione della conoscenza** è trovare qualche modo efficace di rappresentare la conoscenza all'interno di un computer in forma di dati, per renderla automaticamente utilizzabile. Questo può essere visto come uno spettro: -![Spettro della rappresentazione della conoscenza](../../../../translated_images/it/knowledge-spectrum.b60df631852c0217.webp) +![Spettro della rappresentazione della conoscenza](../../../../../../translated_images/it/knowledge-spectrum.b60df631852c0217.webp) > Immagine di [Dmitry Soshnikov](http://soshnikov.com) -* A sinistra, ci sono tipi molto semplici di rappresentazioni della conoscenza che possono essere utilizzati efficacemente dai computer. La più semplice è quella algoritmica, in cui la conoscenza è rappresentata da un programma informatico. Tuttavia, questo non è il modo migliore per rappresentare la conoscenza, perché non è flessibile. La conoscenza nella nostra mente è spesso non algoritmica. -* A destra, ci sono rappresentazioni come il testo naturale. È la più potente, ma non può essere utilizzata per il ragionamento automatico. +* A sinistra, ci sono tipi molto semplici di rappresentazione della conoscenza che possono essere efficacemente usati dai computer. Il più semplice è algoritmico, quando la conoscenza è rappresentata da un programma per computer. Questo, però, non è il miglior modo di rappresentare la conoscenza, perché non è flessibile. La conoscenza nella nostra testa è spesso non algoritmica. +* A destra, ci sono rappresentazioni come il testo naturale. È il più potente, ma non può essere usato per ragionamenti automatici. -> ✅ Pensa per un momento a come rappresenti la conoscenza nella tua mente e la converti in appunti. Esiste un formato particolare che funziona bene per te per favorire la memorizzazione? +> ✅ Pensa un attimo a come rappresenti la conoscenza nella tua testa e la converti in appunti. C'è un formato particolare che funziona bene per te per aiutare nella memorizzazione? -## Classificazione delle Rappresentazioni della Conoscenza nei Computer +## Classificazione delle Rappresentazioni della Conoscenza al Computer -Possiamo classificare i diversi metodi di rappresentazione della conoscenza nei computer nelle seguenti categorie: +Possiamo classificare i diversi metodi di rappresentazione della conoscenza al computer nelle seguenti categorie: -* **Rappresentazioni a rete** si basano sul fatto che abbiamo una rete di concetti interrelati nella nostra mente. Possiamo provare a riprodurre le stesse reti come un grafo all'interno di un computer - una cosiddetta **rete semantica**. +* **Rappresentazioni a rete** si basano sul fatto che abbiamo una rete di concetti interrelati nella nostra testa. Possiamo cercare di riprodurre le stesse reti come un grafo all'interno di un computer - una cosiddetta **rete semantica**. -1. **Triplette Oggetto-Attributo-Valore** o **coppie attributo-valore**. Poiché un grafo può essere rappresentato all'interno di un computer come un elenco di nodi e archi, possiamo rappresentare una rete semantica con un elenco di triplette contenenti oggetti, attributi e valori. Ad esempio, costruiamo le seguenti triplette sui linguaggi di programmazione: +1. **Triplette Oggetto-Attributo-Valore** o **coppie attributo-valore**. Poiché un grafo può essere rappresentato in un computer come una lista di nodi e archi, possiamo rappresentare una rete semantica tramite una lista di triplette, contenenti oggetti, attributi e valori. Per esempio, costruiamo le seguenti triplette su linguaggi di programmazione: Oggetto | Attributo | Valore --------|-----------|------- -Python | è | Linguaggio non tipizzato +Python | è | Linguaggio non tipizzato Python | inventato-da | Guido van Rossum -Python | sintassi-blocco | indentazione +Python | sintassi blocco | indentazione Linguaggio non tipizzato | non ha | definizioni di tipo -> ✅ Pensa a come le triplette possono essere utilizzate per rappresentare altri tipi di conoscenza. +> ✅ Pensa a come le triplette possono essere usate per rappresentare altri tipi di conoscenza. -2. **Rappresentazioni gerarchiche** enfatizzano il fatto che spesso creiamo una gerarchia di oggetti nella nostra mente. Ad esempio, sappiamo che il canarino è un uccello e che tutti gli uccelli hanno le ali. Abbiamo anche un'idea del colore tipico di un canarino e della sua velocità di volo. +2. **Rappresentazioni gerarchiche** enfatizzano il fatto che spesso creiamo una gerarchia di oggetti nella nostra mente. Per esempio, sappiamo che il canarino è un uccello, e tutti gli uccelli hanno le ali. Abbiamo anche una certa idea di quale colore ha solitamente un canarino, e qual è la velocità di volo. - - **Rappresentazione a frame** si basa sulla rappresentazione di ogni oggetto o classe di oggetti come un **frame** che contiene **slot**. Gli slot hanno possibili valori predefiniti, restrizioni sui valori o procedure memorizzate che possono essere chiamate per ottenere il valore di uno slot. Tutti i frame formano una gerarchia simile a quella degli oggetti nei linguaggi di programmazione orientati agli oggetti. - - **Scenari** sono un tipo speciale di frame che rappresentano situazioni complesse che possono evolversi nel tempo. + - La **rappresentazione a frame** si basa sul rappresentare ogni oggetto o classe di oggetti come un **frame** che contiene **slot**. Gli slot possono avere valori predefiniti possibili, restrizioni sul valore, o procedure memorizzate che possono essere chiamate per ottenere il valore di uno slot. Tutti i frame formano una gerarchia simile a una gerarchia degli oggetti nei linguaggi di programmazione orientati agli oggetti. + - I **scenario** sono un tipo speciale di frame che rappresentano situazioni complesse che possono svolgersi nel tempo. **Python** Slot | Valore | Valore predefinito | Intervallo | ------|-------|--------------------|------------| -Nome | Python | | | -È-Un | Linguaggio non tipizzato | | | -Case Variabili | | CamelCase | | -Lunghezza Programma | | | 5-5000 righe | -Sintassi Blocco | Indentazione | | | +-----|---------|--------------------|------------| +Nome | Python | | | +È-Un | Linguaggio non tipizzato | | | +Variabile case | | CamelCase | | +Lunghezza programma | | | 5-5000 righe | +Sintassi blocco | Indentazione | | | -3. **Rappresentazioni procedurali** si basano sulla rappresentazione della conoscenza tramite un elenco di azioni che possono essere eseguite quando si verifica una certa condizione. - - Le regole di produzione sono dichiarazioni if-then che ci permettono di trarre conclusioni. Ad esempio, un medico può avere una regola che dice che **SE** un paziente ha febbre alta **O** un alto livello di proteina C-reattiva nel test del sangue **ALLORA** ha un'infiammazione. Una volta che incontriamo una delle condizioni, possiamo trarre una conclusione sull'infiammazione e usarla nel ragionamento successivo. - - Gli algoritmi possono essere considerati un'altra forma di rappresentazione procedurale, anche se quasi mai vengono utilizzati direttamente nei sistemi basati sulla conoscenza. +3. **Rappresentazioni procedurali** si basano sul rappresentare la conoscenza tramite una lista di azioni che possono essere eseguite quando si verifica una certa condizione. + - Le regole di produzione sono affermazioni di tipo se-allora che ci permettono di trarre conclusioni. Per esempio, un medico può avere una regola che dice che **SE** un paziente ha febbre alta **O** un alto livello di proteina C-reattiva nell'analisi del sangue **ALLORA** ha un'infiammazione. Quando incontriamo una delle condizioni, possiamo trarre una conclusione sull'infiammazione, e poi usarla nel ragionamento successivo. + - Gli algoritmi possono essere considerati un'altra forma di rappresentazione procedurale, anche se sono quasi mai usati direttamente nei sistemi basati sulla conoscenza. -4. **Logica** fu originariamente proposta da Aristotele come un modo per rappresentare la conoscenza universale umana. - - La Logica Predicativa come teoria matematica è troppo ricca per essere computabile, quindi normalmente si utilizza un suo sottoinsieme, come le clausole di Horn usate in Prolog. - - La Logica Descrittiva è una famiglia di sistemi logici utilizzati per rappresentare e ragionare su gerarchie di oggetti e rappresentazioni della conoscenza distribuita, come il *web semantico*. +4. La **Logica** fu originariamente proposta da Aristotele come modo per rappresentare la conoscenza universale umana. + - La Logica dei predicati come teoria matematica è troppo ricca per essere computabile, quindi normalmente viene usato un sottoinsieme, come le clausole di Horn usate in Prolog. + - La Logica descrittiva è una famiglia di sistemi logici usati per rappresentare e ragionare su gerarchie di oggetti e rappresentazioni della conoscenza distribuite come il *semantic web*. ## Sistemi Esperti -Uno dei primi successi dell'IA simbolica furono i cosiddetti **sistemi esperti** - sistemi informatici progettati per agire come esperti in un dominio di problemi limitato. Si basavano su una **base di conoscenza** estratta da uno o più esperti umani e contenevano un **motore di inferenza** che eseguiva un ragionamento su di essa. +Uno dei primi successi dell'IA simbolica furono i cosiddetti **sistemi esperti** - sistemi computerizzati progettati per agire come esperti in un dominio di problemi limitato. Essi si basavano su una **base di conoscenza** estratta da uno o più esperti umani, e contenevano un **motore di inferenza** che eseguiva del ragionamento su di essa. -![Architettura umana](../../../../translated_images/it/arch-human.5d4d35f1bba3ab1c.webp) | ![Sistema basato sulla conoscenza](../../../../translated_images/it/arch-kbs.3ec5c150b09fa8da.webp) +![Architettura Umana](../../../../../../translated_images/it/arch-human.5d4d35f1bba3ab1c.webp) | ![Sistema Basato su Conoscenza](../../../../../../translated_images/it/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ -Struttura semplificata del sistema neurale umano | Architettura di un sistema basato sulla conoscenza +Struttura semplificata di un sistema neurale umano | Architettura di un sistema basato su conoscenza -I sistemi esperti sono costruiti come il sistema di ragionamento umano, che contiene **memoria a breve termine** e **memoria a lungo termine**. Allo stesso modo, nei sistemi basati sulla conoscenza distinguiamo i seguenti componenti: +I sistemi esperti sono costruiti come il sistema di ragionamento umano, che contiene **memoria a breve termine** e **memoria a lungo termine**. Analogamente, nei sistemi basati sulla conoscenza distinguiamo i seguenti componenti: -* **Memoria del problema**: contiene la conoscenza sul problema attualmente in fase di risoluzione, ad esempio la temperatura o la pressione sanguigna di un paziente, se ha un'infiammazione o meno, ecc. Questa conoscenza è anche chiamata **conoscenza statica**, perché contiene un'istantanea di ciò che sappiamo attualmente sul problema - il cosiddetto *stato del problema*. -* **Base di conoscenza**: rappresenta la conoscenza a lungo termine su un dominio di problemi. È estratta manualmente dagli esperti umani e non cambia da una consultazione all'altra. Poiché ci permette di navigare da uno stato del problema a un altro, è anche chiamata **conoscenza dinamica**. -* **Motore di inferenza**: orchestra l'intero processo di ricerca nello spazio degli stati del problema, ponendo domande all'utente quando necessario. È anche responsabile di trovare le regole giuste da applicare a ogni stato. +* **Memoria problema**: contiene la conoscenza sul problema attualmente risolto, cioè la temperatura o la pressione sanguigna di un paziente, se ha infiammazione o no, etc. Questa conoscenza è anche chiamata **conoscenza statica**, perché contiene un'istantanea di ciò che sappiamo al momento sul problema - lo stato del problema. +* **Base di conoscenza**: rappresenta la conoscenza a lungo termine su un dominio di problema. Viene estratta manualmente dagli esperti umani, e non cambia da consultazione a consultazione. Poiché ci permette di navigare da uno stato del problema a un altro, è anche chiamata **conoscenza dinamica**. +* **Motore di inferenza**: orchestra l'intero processo di ricerca nello spazio degli stati del problema, facendo domande all'utente quando necessario. È anche responsabile di trovare le regole giuste da applicare a ogni stato. -Come esempio, consideriamo il seguente sistema esperto per determinare un animale basandosi sulle sue caratteristiche fisiche: +Come esempio, consideriamo il seguente sistema esperto per determinare un animale basato sulle sue caratteristiche fisiche: -![Albero AND-OR](../../../../translated_images/it/AND-OR-Tree.5592d2c70187f283.webp) +![Albero AND-OR](../../../../../../translated_images/it/AND-OR-Tree.5592d2c70187f283.webp) > Immagine di [Dmitry Soshnikov](http://soshnikov.com) -Questo diagramma è chiamato **albero AND-OR**, ed è una rappresentazione grafica di un insieme di regole di produzione. Disegnare un albero è utile all'inizio dell'estrazione della conoscenza dall'esperto. Per rappresentare la conoscenza all'interno del computer è più conveniente utilizzare regole: +Questo diagramma si chiama **albero AND-OR**, ed è una rappresentazione grafica di un insieme di regole di produzione. Disegnare un albero è utile all'inizio dell'estrazione della conoscenza dall'esperto. Per rappresentare la conoscenza all'interno del computer è più conveniente usare le regole: ``` IF the animal eats meat @@ -121,78 +121,78 @@ OR (animal has sharp teeth THEN the animal is a carnivore ``` -Puoi notare che ogni condizione sul lato sinistro della regola e l'azione sono essenzialmente triplette Oggetto-Attributo-Valore (OAV). La **memoria di lavoro** contiene l'insieme di triplette OAV che corrispondono al problema attualmente in fase di risoluzione. Un **motore di regole** cerca regole per le quali una condizione è soddisfatta e le applica, aggiungendo un'altra tripletta alla memoria di lavoro. +Puoi notare che ogni condizione sul lato sinistro della regola e l'azione sono essenzialmente triplette oggetto-attributo-valore (OAV). La **memoria di lavoro** contiene l'insieme delle triplette OAV che corrispondono al problema attualmente risolto. Un **motore di regole** cerca regole per cui una condizione è soddisfatta e le applica, aggiungendo un'altra tripletta alla memoria di lavoro. > ✅ Scrivi il tuo albero AND-OR su un argomento che ti piace! -### Inferenza Avanti vs. Indietro +### Inferenza Diretta vs. Inversa -Il processo descritto sopra è chiamato **inferenza avanti**. Inizia con alcuni dati iniziali sul problema disponibili nella memoria di lavoro e poi esegue il seguente ciclo di ragionamento: +Il processo descritto sopra si chiama **inferenza diretta**. Inizia con alcuni dati iniziali sul problema disponibili nella memoria di lavoro, e poi esegue il seguente ciclo di ragionamento: -1. Se l'attributo target è presente nella memoria di lavoro - fermati e fornisci il risultato -2. Cerca tutte le regole le cui condizioni sono attualmente soddisfatte - ottieni il **set di conflitto** delle regole. -3. Esegui la **risoluzione del conflitto** - seleziona una regola che verrà eseguita in questo passaggio. Ci possono essere diverse strategie di risoluzione del conflitto: +1. Se l'attributo obiettivo è presente nella memoria di lavoro - fermati e dai il risultato +2. Cerca tutte le regole la cui condizione è attualmente soddisfatta - ottieni un **insieme di conflitto** di regole. +3. Esegui la **risoluzione del conflitto** - seleziona una regola che sarà eseguita in questo passo. Ci possono essere diverse strategie di risoluzione del conflitto: - Seleziona la prima regola applicabile nella base di conoscenza - - Seleziona una regola casuale - - Seleziona una regola *più specifica*, cioè quella che soddisfa il maggior numero di condizioni nel "lato sinistro" (LHS) + - Seleziona una regola a caso + - Seleziona una regola *più specifica*, cioè quella che soddisfa più condizioni nella "parte sinistra" (LHS) 4. Applica la regola selezionata e inserisci un nuovo pezzo di conoscenza nello stato del problema 5. Ripeti dal passo 1. -Tuttavia, in alcuni casi potremmo voler iniziare con una conoscenza vuota sul problema e porre domande che ci aiutino a giungere alla conclusione. Ad esempio, durante una diagnosi medica, di solito non eseguiamo tutte le analisi mediche in anticipo prima di iniziare a diagnosticare il paziente. Piuttosto, vogliamo eseguire analisi quando è necessario prendere una decisione. +Tuttavia, in alcuni casi potremmo voler partire da una conoscenza vuota sul problema, e fare domande che ci aiutino a raggiungere la conclusione. Per esempio, quando si fa una diagnosi medica, di solito non eseguiamo tutte le analisi mediche in anticipo prima di iniziare a diagnosticare il paziente. Piuttosto, vogliamo eseguire le analisi quando bisogna prendere una decisione. -Questo processo può essere modellato utilizzando **inferenza indietro**. È guidato dal **goal** - il valore dell'attributo che stiamo cercando di trovare: +Questo processo può essere modellato usando l'**inferenza inversa**. È guidata dal **goal** - il valore dell'attributo che vogliamo trovare: -1. Seleziona tutte le regole che possono fornire il valore di un goal (cioè con il goal sul RHS ("lato destro")) - un set di conflitto +1. Seleziona tutte le regole che possono darci il valore di un goal (cioè con il goal sul RHS ("right-hand-side")) - un insieme di conflitto 1. Se non ci sono regole per questo attributo, o c'è una regola che dice che dovremmo chiedere il valore all'utente - chiedilo, altrimenti: -1. Usa una strategia di risoluzione del conflitto per selezionare una regola che useremo come *ipotesi* - proveremo a dimostrarla +1. Usa la strategia di risoluzione del conflitto per selezionare una regola che useremo come *ipotesi* - proveremo a dimostrarla 1. Ripeti ricorsivamente il processo per tutti gli attributi nel LHS della regola, cercando di dimostrarli come goal 1. Se in qualsiasi momento il processo fallisce - usa un'altra regola al passo 3. -> ✅ In quali situazioni è più appropriata l'inferenza avanti? E l'inferenza indietro? +> ✅ In quali situazioni l'inferenza diretta è più appropriata? E l'inferenza inversa? -### Implementazione dei Sistemi Esperti +### Implementare Sistemi Esperti -I sistemi esperti possono essere implementati utilizzando diversi strumenti: +I sistemi esperti possono essere implementati usando diversi strumenti: -* Programmandoli direttamente in un linguaggio di programmazione di alto livello. Questo non è l'approccio migliore, perché il principale vantaggio di un sistema basato sulla conoscenza è che la conoscenza è separata dall'inferenza, e potenzialmente un esperto del dominio del problema dovrebbe essere in grado di scrivere regole senza comprendere i dettagli del processo di inferenza. -* Utilizzando un **guscio per sistemi esperti**, cioè un sistema progettato specificamente per essere popolato con conoscenza utilizzando un linguaggio di rappresentazione della conoscenza. +* Programmandoli direttamente in un linguaggio di programmazione ad alto livello. Questa non è la migliore idea, perché il vantaggio principale di un sistema basato sulla conoscenza è che la conoscenza è separata dall'inferenza, e potenzialmente un esperto del dominio del problema dovrebbe essere in grado di scrivere regole senza comprendere i dettagli del processo di inferenza. +* Usando una **shell per sistemi esperti**, cioè un sistema specificamente progettato per essere popolato di conoscenza usando qualche linguaggio di rappresentazione della conoscenza. -## ✍️ Esercizio: Inferenza sugli Animali +## ✍️ Esercizio: Inferenza Animale -Consulta [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) per un esempio di implementazione di un sistema esperto con inferenza avanti e indietro. +Vedi [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) per un esempio di implementazione di un sistema esperto con inferenza diretta e inversa. -> **Nota**: Questo esempio è piuttosto semplice e offre solo un'idea di come appare un sistema esperto. Una volta che inizi a creare un sistema del genere, noterai un comportamento *intelligente* solo quando raggiungi un certo numero di regole, circa 200+. A un certo punto, le regole diventano troppo complesse per tenerle tutte a mente, e a quel punto potresti iniziare a chiederti perché il sistema prende certe decisioni. Tuttavia, una caratteristica importante dei sistemi basati sulla conoscenza è che puoi sempre *spiegare* esattamente come è stata presa una decisione. +> **Nota**: Questo esempio è piuttosto semplice, e dà solo l'idea di come è fatto un sistema esperto. Una volta che inizi a creare un sistema del genere, noterai un comportamento *intelligente* solo quando raggiungi un certo numero di regole, circa 200+. A un certo punto, le regole diventano troppo complesse per tenerle tutte a mente, e a quel punto potresti iniziare a chiederti perché un sistema prende certe decisioni. Tuttavia, la caratteristica importante dei sistemi basati sulla conoscenza è che puoi sempre *spiegare* esattamente come è stata presa una qualsiasi decisione. -## Ontologie e Web Semantico +## Ontologie e il Web Semantico -Alla fine del XX secolo c'è stata un'iniziativa per utilizzare la rappresentazione della conoscenza per annotare le risorse Internet, in modo che fosse possibile trovare risorse che corrispondessero a query molto specifiche. Questo movimento è stato chiamato **Web Semantico**, e si basava su diversi concetti: +Alla fine del XX secolo c'è stata un'iniziativa di usare la rappresentazione della conoscenza per annotare risorse Internet, in modo che fosse possibile trovare risorse che corrispondessero a query molto specifiche. Questo movimento si chiamava **Web Semantico**, e si basava su diversi concetti: -- Una rappresentazione della conoscenza speciale basata su **[logiche descrittive](https://en.wikipedia.org/wiki/Description_logic)** (DL). È simile alla rappresentazione della conoscenza a frame, perché costruisce una gerarchia di oggetti con proprietà, ma ha una semantica logica formale e inferenza. Esiste un'intera famiglia di DL che bilanciano tra espressività e complessità algoritmica dell'inferenza. -- Rappresentazione della conoscenza distribuita, dove tutti i concetti sono rappresentati da un identificatore URI globale, rendendo possibile creare gerarchie di conoscenza che si estendono su Internet. +- Una rappresentazione della conoscenza speciale basata su **[logiche descrittive](https://en.wikipedia.org/wiki/Description_logic)** (DL). È simile alla rappresentazione della conoscenza a frame, perché costruisce una gerarchia di oggetti con proprietà, ma ha una semantica logica formale e inferenza. Esiste un'intera famiglia di DL che bilancia tra espressività e complessità algoritmica dell'inferenza. +- Rappresentazione della conoscenza distribuita, dove tutti i concetti sono rappresentati da un identificatore URI globale, consentendo di creare gerarchie di conoscenza che attraversano Internet. - Una famiglia di linguaggi basati su XML per la descrizione della conoscenza: RDF (Resource Description Framework), RDFS (RDF Schema), OWL (Ontology Web Language). -Un concetto centrale nel Web Semantico è il concetto di **Ontologia**. Si riferisce a una specifica esplicita di un dominio di problemi utilizzando una rappresentazione formale della conoscenza. L'ontologia più semplice può essere semplicemente una gerarchia di oggetti in un dominio di problemi, ma le ontologie più complesse includeranno regole che possono essere utilizzate per l'inferenza. +Un concetto fondamentale nel Web Semantico è il concetto di **Ontologia**. Si riferisce a una specifica esplicita di un dominio problematica utilizzando una rappresentazione formale della conoscenza. L'ontologia più semplice può essere solo una gerarchia di oggetti in un dominio problematico, ma ontologie più complesse includeranno regole che possono essere utilizzate per inferenza. -Nel Web Semantico, tutte le rappresentazioni si basano su triplette. Ogni oggetto e ogni relazione sono identificati in modo univoco tramite un URI. Ad esempio, se vogliamo affermare il fatto che questo Curriculum AI è stato sviluppato da Dmitry Soshnikov il 1° gennaio 2022, ecco le triplette che possiamo utilizzare: +Nel web semantico, tutte le rappresentazioni si basano su triplette. Ogni oggetto e ogni relazione sono univocamente identificati dall'URI. Per esempio, se vogliamo affermare il fatto che questo Curriculum AI è stato sviluppato da Dmitry Soshnikov il 1° gennaio 2022 - ecco le triplette che possiamo usare: - + ``` -http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 13, 2007” +http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 1, 2022” http://github.com/microsoft/ai-for-beginners http://purl.org/dc/elements/1.1/creator http://soshnikov.com ``` > ✅ Qui `http://www.example.com/terms/creation-date` e `http://purl.org/dc/elements/1.1/creator` sono alcuni URI ben noti e universalmente accettati per esprimere i concetti di *creatore* e *data di creazione*. -In un caso più complesso, se vogliamo definire un elenco di creatori, possiamo utilizzare alcune strutture dati definite in RDF. +In un caso più complesso, se vogliamo definire una lista di creatori, possiamo usare alcune strutture dati definite in RDF. - + > Diagrammi sopra di [Dmitry Soshnikov](http://soshnikov.com) -Il progresso nella costruzione del Web Semantico è stato in qualche modo rallentato dal successo dei motori di ricerca e delle tecniche di elaborazione del linguaggio naturale, che consentono di estrarre dati strutturati dal testo. Tuttavia, in alcune aree ci sono ancora sforzi significativi per mantenere ontologie e basi di conoscenza. Alcuni progetti degni di nota: +Il progresso nella costruzione del Web Semantico è stato in qualche modo rallentato dal successo dei motori di ricerca e delle tecniche di elaborazione del linguaggio naturale, che consentono di estrarre dati strutturati dal testo. Tuttavia, in alcune aree ci sono ancora sforzi significativi per mantenere ontologie e basi di conoscenza. Alcuni progetti da segnalare: -* [WikiData](https://wikidata.org/) è una raccolta di basi di conoscenza leggibili dalle macchine associate a Wikipedia. La maggior parte dei dati viene estratta dagli *InfoBox* di Wikipedia, frammenti di contenuto strutturato all'interno delle pagine di Wikipedia. Puoi [interrogare](https://query.wikidata.org/) WikiData in SPARQL, un linguaggio di query speciale per il Web Semantico. Ecco un esempio di query che mostra i colori degli occhi più popolari tra gli esseri umani: +* [WikiData](https://wikidata.org/) è una raccolta di basi di conoscenza leggibili da macchina associate a Wikipedia. La maggior parte dei dati è estratta dalle *InfoBoxes* di Wikipedia, pezzi di contenuto strutturato all'interno delle pagine di Wikipedia. È possibile [interrogare](https://query.wikidata.org/) wikidata in SPARQL, un linguaggio di query speciale per il Web Semantico. Ecco una query di esempio che mostra i colori degli occhi più popolari tra gli esseri umani: ```sparql #defaultView:BubbleChart @@ -206,47 +206,51 @@ WHERE GROUP BY ?eyeColorLabel ``` -* [DBpedia](https://www.dbpedia.org/) è un altro progetto simile a WikiData. +* [DBpedia](https://www.dbpedia.org/) è un altro sforzo simile a WikiData. -> ✅ Se vuoi sperimentare la costruzione di ontologie personali o aprire quelle esistenti, c'è un ottimo editor visivo di ontologie chiamato [Protégé](https://protege.stanford.edu/). Scaricalo o usalo online. +> ✅ Se vuoi sperimentare la costruzione delle tue ontologie, o aprire quelle esistenti, c'è un ottimo editor visivo di ontologie chiamato [Protégé](https://protege.stanford.edu/). Scaricalo o usalo online. - + -*Editor Web Protégé aperto con l'ontologia della famiglia Romanov. Screenshot di Dmitry Soshnikov* +*Editor Web Protégé aperto con l'ontologia della Famiglia Romanov. Screenshot di Dmitry Soshnikov* -## ✍️ Esercizio: Un'Ontologia Familiare +## ✍️ Esercizio: Un'ontologia familiare -Consulta [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) per un esempio di utilizzo delle tecniche del Web Semantico per ragionare sulle relazioni familiari. Prenderemo un albero genealogico rappresentato nel formato comune GEDCOM e un'ontologia delle relazioni familiari e costruiremo un grafo di tutte le relazioni familiari per un determinato insieme di individui. +Consulta [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) per un esempio di utilizzo delle tecniche del Web Semantico per ragionare sulle relazioni familiari. Prenderemo un albero genealogico rappresentato nel formato GEDCOM comune e un'ontologia delle relazioni familiari e costruiremo un grafo di tutte le relazioni familiari per un dato insieme di individui. ## Microsoft Concept Graph -Nella maggior parte dei casi, le ontologie sono create con cura manualmente. Tuttavia, è anche possibile **estrarre** ontologie da dati non strutturati, ad esempio da testi in linguaggio naturale. +Nella maggior parte dei casi, le ontologie sono create manualmente con cura. Tuttavia, è anche possibile **estrarre** ontologie da dati non strutturati, per esempio, da testi in linguaggio naturale. -Uno di questi tentativi è stato fatto da Microsoft Research, e ha portato al [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). +Un tentativo del genere è stato fatto da Microsoft Research, che ha prodotto [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). -Si tratta di una vasta raccolta di entità raggruppate utilizzando la relazione di ereditarietà `is-a`. Consente di rispondere a domande come "Cos'è Microsoft?" - la risposta potrebbe essere qualcosa come "una compagnia con probabilità 0.87, e un marchio con probabilità 0.75". +Si tratta di una grande raccolta di entità raggruppate insieme usando la relazione di ereditarietà `is-a`. Consente di rispondere a domande come "Cos'è Microsoft?" - la risposta è qualcosa come "una compagnia con probabilità 0.87, e un marchio con probabilità 0.75". -Il grafo è disponibile sia come API REST, sia come un grande file di testo scaricabile che elenca tutte le coppie di entità. +Il Grafo è disponibile sia come API REST, sia come un grande file di testo scaricabile che elenca tutte le coppie di entità. -## ✍️ Esercizio: Un Concept Graph +## ✍️ Esercizio: Un grafo concettuale -Prova il notebook [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) per vedere come possiamo utilizzare Microsoft Concept Graph per raggruppare articoli di notizie in diverse categorie. +Prova il notebook [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) per vedere come possiamo usare Microsoft Concept Graph per raggruppare articoli di notizie in diverse categorie. ## Conclusione -Oggi, l'IA è spesso considerata sinonimo di *Machine Learning* o *Reti Neurali*. Tuttavia, un essere umano mostra anche un ragionamento esplicito, che è qualcosa che attualmente non viene gestito dalle reti neurali. Nei progetti del mondo reale, il ragionamento esplicito viene ancora utilizzato per svolgere compiti che richiedono spiegazioni o la capacità di modificare il comportamento del sistema in modo controllato. +Oggigiorno, l'AI è spesso considerata sinonimo di *Machine Learning* o *Reti Neurali*. Tuttavia, un essere umano manifesta anche ragionamento esplicito, qualcosa che attualmente non viene gestito dalle reti neurali. Nei progetti reali, il ragionamento esplicito è ancora usato per svolgere compiti che richiedono spiegazioni o la capacità di modificare il comportamento del sistema in modo controllato. ## 🚀 Sfida -Nel notebook sull'Ontologia Familiare associato a questa lezione, c'è l'opportunità di sperimentare altre relazioni familiari. Prova a scoprire nuove connessioni tra le persone nell'albero genealogico. +Nel notebook Ontologia Familiare associato a questa lezione, c'è la possibilità di sperimentare con altre relazioni familiari. Prova a scoprire nuove connessioni tra le persone nell'albero genealogico. ## [Quiz post-lezione](https://ff-quizzes.netlify.app/en/ai/quiz/4) -## Revisione & Studio Autonomo +## Revisione & Studio autonomo -Fai qualche ricerca su internet per scoprire aree in cui gli esseri umani hanno cercato di quantificare e codificare la conoscenza. Dai un'occhiata alla Tassonomia di Bloom e torna indietro nella storia per imparare come gli esseri umani hanno cercato di dare un senso al loro mondo. Esplora il lavoro di Linneo per creare una tassonomia degli organismi e osserva il modo in cui Dmitri Mendeleev ha creato un sistema per descrivere e raggruppare gli elementi chimici. Quali altri esempi interessanti riesci a trovare? +Fai qualche ricerca su internet per scoprire le aree in cui gli esseri umani hanno cercato di quantificare e codificare la conoscenza. Dai un'occhiata alla Tassonomia di Bloom e torna indietro nella storia per imparare come gli esseri umani hanno cercato di dare un senso al loro mondo. Esplora il lavoro di Linneo per creare una tassonomia degli organismi e osserva il modo in cui Dmitri Mendeleev ha creato un metodo per descrivere e raggruppare gli elementi chimici. Quali altri esempi interessanti riesci a trovare? -**Compito**: [Costruisci un'Ontologia](assignment.md) +**Compito**: [Costruire un'ontologia](assignment.md) --- + +**Disclaimers**: +Questo documento è stato tradotto utilizzando il servizio di traduzione AI [Co-op Translator](https://github.com/Azure/co-op-translator). Pur impegnandoci per garantire l’accuratezza, si prega di notare che le traduzioni automatiche possono contenere errori o imprecisioni. Il documento originale nella sua lingua natia deve essere considerato la fonte autorevole. Per informazioni critiche, si raccomanda una traduzione professionale effettuata da un traduttore umano. Non siamo responsabili per eventuali malintesi o interpretazioni errate derivanti dall’uso di questa traduzione. + \ No newline at end of file diff --git a/translations/pl/README.md b/translations/pl/README.md index f9c6f551..f6bd7c34 100644 --- a/translations/pl/README.md +++ b/translations/pl/README.md @@ -1,8 +1,8 @@ [Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Chinese (Simplified)](../zh/README.md) | [Chinese (Traditional, Hong Kong)](../hk/README.md) | [Chinese (Traditional, Macau)](../mo/README.md) | [Chinese (Traditional, Taiwan)](../tw/README.md) | [Croatian](../hr/README.md) | [Czech](../cs/README.md) | [Danish](../da/README.md) | [Dutch](../nl/README.md) | [Estonian](../et/README.md) | [Finnish](../fi/README.md) | [French](../fr/README.md) | [German](../de/README.md) | [Greek](../el/README.md) | [Hebrew](../he/README.md) | [Hindi](../hi/README.md) | [Hungarian](../hu/README.md) | [Indonesian](../id/README.md) | [Italian](../it/README.md) | [Japanese](../ja/README.md) | [Kannada](../kn/README.md) | [Korean](../ko/README.md) | [Lithuanian](../lt/README.md) | [Malay](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Nigerian Pidgin](../pcm/README.md) | [Norwegian](../no/README.md) | [Persian (Farsi)](../fa/README.md) | [Polish](./README.md) | [Portuguese (Brazil)](../br/README.md) | [Portuguese (Portugal)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romanian](../ro/README.md) | [Russian](../ru/README.md) | [Serbian (Cyrillic)](../sr/README.md) | [Slovak](../sk/README.md) | [Slovenian](../sl/README.md) | [Spanish](../es/README.md) | [Swahili](../sw/README.md) | [Swedish](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Turkish](../tr/README.md) | [Ukrainian](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamese](../vi/README.md) > **Wolisz klonować lokalnie?** -> To repozytorium zawiera ponad 50 tłumaczeń, co znacząco zwiększa rozmiar pobierania. Aby sklonować bez tłumaczeń, użyj sparse checkout: +> To repozytorium zawiera ponad 50 tłumaczeń językowych, co znacznie zwiększa rozmiar pobieranego pliku. Aby sklonować bez tłumaczeń, użyj sparse checkout: > ```bash > git clone --filter=blob:none --sparse https://github.com/microsoft/AI-For-Beginners.git > cd AI-For-Beginners > git sparse-checkout set --no-cone '/*' '!translations' '!translated_images' > ``` -> W ten sposób uzyskasz wszystko, czego potrzebujesz do ukończenia kursu z dużo szybszym pobraniem. +> To zapewni ci wszystko, co potrzebne do ukończenia kursu z dużo szybszym pobieraniem. -**Jeśli chcesz, aby dodatkowe języki tłumaczeń były obsługiwane, są one wymienione [tutaj](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** +**Jeśli chcesz, aby obsługiwane były dodatkowe języki tłumaczeń są one wymienione [tutaj](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** ## Dołącz do społeczności [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) ## Czego się nauczysz -**[Mapa mentalna kursu](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** +**[Mapa myśli kursu](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** W tym programie nauczania nauczysz się: -* Różnych podejść do Sztucznej Inteligencji, w tym "dobrej, starej" podejścia symbolicznego z **Reprezentacją Wiedzy** i wnioskowaniem ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). -* **Sieci neuronowych** i **uczenia głębokiego**, które są podstawą współczesnej AI. Pokażemy koncepcje stojące za tymi ważnymi tematami za pomocą kodu w dwóch najpopularniejszych frameworkach - [TensorFlow](http://Tensorflow.org) i [PyTorch](http://pytorch.org). -* **Architektury neuronowe** do pracy z obrazami i tekstem. Omówimy najnowsze modele, ale mogą one nie być w pełni zgodne ze stanem wiedzy. -* Mniej popularne podejścia do AI, takie jak **algorytmy genetyczne** i **systemy wieloagentowe**. +* Różnych podejść do sztucznej inteligencji, w tym "dobrej starej" symbolicznej metody z **Reprezentacją Wiedzy** i wnioskowaniem ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). +* **Sieci neuronowych** i **uczenia głębokiego**, które są u podstaw nowoczesnej AI. Pokażemy koncepcje stojące za tymi ważnymi tematami używając kodu w dwóch najpopularniejszych frameworkach - [TensorFlow](http://Tensorflow.org) i [PyTorch](http://pytorch.org). +* **Architektury neuronowe** do pracy z obrazami i tekstem. Omówimy najnowsze modele, choć mogą one nie obejmować najnowszych osiągnięć. +* Mniej popularne podejścia AI, takie jak **algorytmy genetyczne** i **systemy wieloagentowe**. -Czego nie omówimy w tym programie: +Czego nie obejmuje ten program nauczania: -> [Znajdź wszystkie dodatkowe materiały do tego kursu w naszej kolekcji Microsoft Learn](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) +> [Znajdź wszystkie dodatkowe zasoby do tego kursu w naszej kolekcji Microsoft Learn](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* Przypadków biznesowych zastosowania **AI w biznesie**. Rozważ podjęcie ścieżki nauki [Introduction to AI for business users](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) na Microsoft Learn lub [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), opracowanej we współpracy z [INSEAD](https://www.insead.edu/). -* **Klasycznego uczenia maszynowego**, które jest dobrze opisane w naszym [Machine Learning for Beginners Curriculum](http://github.com/Microsoft/ML-for-Beginners). -* Praktycznych zastosowań AI zbudowanych za pomocą **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. W tym celu zalecamy rozpoczęcie od modułów Microsoft Learn dotyczących [wizji](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [przetwarzania języka naturalnego](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[Generative AI z Azure OpenAI Service](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** i innych. -* Specyficznych chmurowych frameworków ML, takich jak [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum), [Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum) lub [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Rozważ skorzystanie ze ścieżek nauki [Build and operate machine learning solutions with Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) oraz [Build and Operate Machine Learning Solutions with Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum). -* **AI konwersacyjne** i **Chat Boty**. Istnieje osobna ścieżka nauki [Create conversational AI solutions](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum), a także możesz odwołać się do [tego wpisu na blogu](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) w celu uzyskania szczegółowych informacji. -* **Głębokiej matematyki** stojącej za uczeniem głębokim. W tym celu zalecamy książkę [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) autorstwa Iana Goodfellowa, Yoshua Bengio i Aarona Courville'a, która jest również dostępna online pod adresem [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). +* Przypadki biznesowe zastosowania **AI w biznesie**. Rozważ podjęcie ścieżki nauki [Wprowadzenie do AI dla użytkowników biznesowych](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) na Microsoft Learn lub [Szkołę AI dla Biznesu](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), opracowaną we współpracy z [INSEAD](https://www.insead.edu/). +* **Klasyczne uczenie maszynowe**, które jest dobrze opisane w naszym programie [Uczenie maszynowe dla początkujących](http://github.com/Microsoft/ML-for-Beginners). +* Praktyczne zastosowania AI zbudowane przy użyciu **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. W tym celu zalecamy rozpoczęcie od modułów Microsoft Learn dotyczących [wizji komputerowej](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [przetwarzania języka naturalnego](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[Generatywnej AI z Azure OpenAI Service](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** i innych. +* Specyficzne ML **frameworki chmurowe**, takie jak [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum), [Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum) lub [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Rozważ użycie ścieżek nauki [Budowanie i obsługa rozwiązań uczenia maszynowego z Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) oraz [Budowanie i obsługa rozwiązań uczenia maszynowego z Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum). +* **Konwersacyjne AI** i **chatboty**. Istnieje osobna ścieżka nauki [Tworzenie rozwiązań konwersacyjnej AI](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum), a także możesz odnieść się do [tego wpisu na blogu](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) po więcej szczegółów. +* **Głęboka matematyka** stojąca za uczeniem głębokim. W tym celu polecamy [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) autorstwa Iana Goodfellowa, Yoshua Bengio i Aarona Courville, która jest również dostępna online na [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). -Dla łagodnego wprowadzenia do tematów _AI w chmurze_ możesz rozważyć podjęcie ścieżki nauki [Get started with artificial intelligence on Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum). +Dla łagodnego wprowadzenia do tematów _AI w chmurze_ możesz rozważyć odbycie ścieżki nauki [Rozpocznij przygodę ze sztuczną inteligencją w Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum). # Zawartość | | Link do lekcji | PyTorch/Keras/TensorFlow | Laboratorium | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | -| 0 | [Konfiguracja kursu](./lessons/0-course-setup/setup.md) | [Jak skonfigurować środowisko programistyczne](./lessons/0-course-setup/how-to-run.md) | | +| 0 | [Konfiguracja kursu](./lessons/0-course-setup/setup.md) | [Skonfiguruj swoje środowisko programistyczne](./lessons/0-course-setup/how-to-run.md) | | | I | [**Wprowadzenie do AI**](./lessons/1-Intro/README.md) | | | | 01 | [Wprowadzenie i historia AI](./lessons/1-Intro/README.md) | - | - | | II | **Symboliczne AI** | -| 02 | [Reprezentacja wiedzy i systemy ekspertowe](./lessons/2-Symbolic/README.md) | [Systemy ekspertowe](./lessons/2-Symbolic/Animals.ipynb) / [Ontologia](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Graf pojęć](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | +| 02 | [Reprezentacja wiedzy i systemy eksperckie](./lessons/2-Symbolic/README.md) | [Systemy eksperckie](./lessons/2-Symbolic/Animals.ipynb) / [Ontologia](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Graf pojęć](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | | III | [**Wprowadzenie do sieci neuronowych**](./lessons/3-NeuralNetworks/README.md) ||| | 03 | [Perceptron](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [Notebook](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [Lab](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | -| 04 | [Wielowarstwowy Perceptron i Tworzenie własnego Frameworku](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Notebook](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Lab](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | -| 05 | [Wprowadzenie do Frameworków (PyTorch/TensorFlow) i Przeuczenia](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [PyTorch](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [Keras](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | -| IV | [**Widzenie Komputerowe**](./lessons/4-ComputerVision/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [Poznaj Widzenie Komputerowe na Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | -| 06 | [Wprowadzenie do Widzenia Komputerowego. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Notebook](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Lab](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | -| 07 | [Splotowe Sieci Neuronowe](./lessons/4-ComputerVision/07-ConvNets/README.md) & [Architektury CNN](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [Lab](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | -| 08 | [Sieci wstępnie wytrenowane i Transfer Learning](./lessons/4-ComputerVision/08-TransferLearning/README.md) oraz [Triki treningowe](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | -| 09 | [Autoenkodery i VAEs](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | -| 10 | [Generatywne Sieci Adwersarialne i Transfer Stylu Artystycznego](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | -| 11 | [Detekcja Obiektów](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [Lab](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | +| 04 | [Wielowarstwowy perceptron i tworzenie własnego frameworka](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Notebook](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Lab](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | +| 05 | [Wprowadzenie do frameworków (PyTorch/TensorFlow) i przeuczenie](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [PyTorch](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [Keras](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | +| IV | [**Wizja komputerowa**](./lessons/4-ComputerVision/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [Poznaj wizję komputerową na Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | +| 06 | [Wprowadzenie do wizji komputerowej. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Notebook](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Lab](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | +| 07 | [Splotowe sieci neuronowe](./lessons/4-ComputerVision/07-ConvNets/README.md) & [Architektury CNN](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [Lab](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | +| 08 | [Sieci wstępnie wytrenowane i uczenie transferowe](./lessons/4-ComputerVision/08-TransferLearning/README.md) oraz [Sztuczki treningowe](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | +| 09 | [Autoenkodery i VAE](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | +| 10 | [Generatywne sieci przeciwstawne i transfer stylu](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | +| 11 | [Detekcja obiektów](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [Lab](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | | 12 | [Segmentacja semantyczna. U-Net](./lessons/4-ComputerVision/12-Segmentation/README.md) | [PyTorch](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb) | | -| V | [**Przetwarzanie języka naturalnego**](./lessons/5-NLP/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [Poznaj Przetwarzanie języka naturalnego na Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| +| V | [**Przetwarzanie języka naturalnego**](./lessons/5-NLP/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [Poznaj przetwarzanie języka naturalnego na Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| | 13 | [Reprezentacja tekstu. Bow/TF-IDF](./lessons/5-NLP/13-TextRep/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb) | | | 14 | [Semantyczne osadzenia słów. Word2Vec i GloVe](./lessons/5-NLP/14-Embeddings/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb) | | -| 15 | [Modelowanie języka. Trening własnych osadzeń](./lessons/5-NLP/15-LanguageModeling/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [Lab](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | -| 16 | [Rekurencyjne Sieci Neuronowe](./lessons/5-NLP/16-RNN/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNTF.ipynb) | | +| 15 | [Modelowanie języka. Trenowanie własnych osadzeń](./lessons/5-NLP/15-LanguageModeling/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [Lab](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | +| 16 | [Rekurencyjne sieci neuronowe](./lessons/5-NLP/16-RNN/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNTF.ipynb) | | | 17 | [Generatywne sieci rekurencyjne](./lessons/5-NLP/17-GenerativeNetworks/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb) | [Lab](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | | 18 | [Transformery. BERT.](./lessons/5-NLP/18-Transformers/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb) /[TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersTF.ipynb) | | -| 19 | [Rozpoznawanie nazwanych bytów](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [Lab](./lessons/5-NLP/19-NER/lab/README.md) | +| 19 | [Rozpoznawanie nazwanych jednostek](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [Lab](./lessons/5-NLP/19-NER/lab/README.md) | | 20 | [Duże modele językowe, programowanie promptów i zadania few-shot](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | | VI | **Inne techniki AI** || | | 21 | [Algorytmy genetyczne](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Notebook](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | -| 22 | [Deep Reinforcement Learning](./lessons/6-Other/22-DeepRL/README.md) | [PyTorch](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[TensorFlow](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [Lab](./lessons/6-Other/22-DeepRL/lab/README.md) | +| 22 | [Głębokie uczenie ze wzmocnieniem](./lessons/6-Other/22-DeepRL/README.md) | [PyTorch](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[TensorFlow](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [Lab](./lessons/6-Other/22-DeepRL/lab/README.md) | | 23 | [Systemy wieloagentowe](./lessons/6-Other/23-MultiagentSystems/README.md) | | | | VII | **Etyka AI** | | | -| 24 | [Etyka AI i odpowiedzialne AI](./lessons/7-Ethics/README.md) | [Microsoft Learn: Zasady odpowiedzialnej AI](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | +| 24 | [Etyka AI i odpowiedzialna sztuczna inteligencja](./lessons/7-Ethics/README.md) | [Microsoft Learn: Zasady odpowiedzialnej AI](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | | IX | **Dodatki** | | | | 25 | [Sieci multimodalne, CLIP i VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Notebook](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | ## Każda lekcja zawiera -* Materiały do wcześniejszej lektury -* Wykonalne notatniki Jupyter, które często są specyficzne dla frameworka (**PyTorch** lub **TensorFlow**). Wykonalny notatnik zawiera również dużo materiału teoretycznego, więc aby zrozumieć temat musisz przejść przez co najmniej jedną wersję notatnika (PyTorch lub TensorFlow). -* **Laboratoria** dostępne dla niektórych tematów, które dają możliwość wypróbowania materiału w praktycznym zadaniu. -* Niektóre sekcje zawierają linki do modułów [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) omawiających powiązane tematy. +* Materiały do wstępnej lektury +* Wykonywalne notatniki Jupyter, często specyficzne dla frameworka (**PyTorch** lub **TensorFlow**). Wykonywalny notatnik zawiera również dużo materiału teoretycznego, więc aby zrozumieć temat, należy przejrzeć przynajmniej jedną wersję notatnika (PyTorch lub TensorFlow). +* **Laboratoria** dostępne dla niektórych tematów, które dają możliwość spróbowania zastosowania poznanego materiału do konkretnego problemu. +* Niektóre sekcje zawierają linki do modułów [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum), które obejmują powiązane tematy. -## Rozpoczęcie +## Rozpoczęcie pracy ### 🎯 Nowy w AI? Zacznij tutaj! -Jeśli jesteś całkowicie nowy w AI i chcesz szybkie, praktyczne przykłady, sprawdź nasze [**Przyjazne dla początkujących przykłady**](./examples/README.md)! Obejmują one: +Jeśli jesteś całkowicie nowy w AI i chcesz szybko poznać praktyczne przykłady, sprawdź nasze [**Przykłady dla początkujących**](./examples/README.md)! Obejmują one: - 🌟 **Hello AI World** - Twój pierwszy program AI (rozpoznawanie wzorców) -- 🧠 **Prosta sieć neuronowa** - Zbuduj sieć neuronową od podstaw -- 🖼️ **Klasyfikator obrazów** - Klasyfikuj obrazy z obszernymi komentarzami -- 💬 **Analiza nastroju tekstu** - Analiza pozytywnego/negatywnego tekstu +- 🧠 **Prosta sieć neuronowa** - Budowa sieci neuronowej od podstaw +- 🖼️ **Klasyfikator obrazów** - Klasyfikacja obrazów z szczegółowymi komentarzami +- 💬 **Analiza nastroju tekstu** - Analiza tekstu pod kątem pozytywnego/negatywnego wydźwięku -Te przykłady mają pomóc Ci zrozumieć koncepcje AI przed zagłębieniem się w pełny program nauczania. +Te przykłady mają na celu pomóc Ci zrozumieć koncepty AI zanim zagłębisz się w pełny program nauczania. -### 📚 Konfiguracja pełnego programu nauczania +### 📚 Pełna konfiguracja programu nauczania -- Stworzyliśmy [lekcję wprowadzającą](./lessons/0-course-setup/setup.md), która pomoże Ci w ustawieniu środowiska programistycznego. - Dla nauczycieli przygotowaliśmy również [lekcję konfiguracyjną programu nauczania](./lessons/0-course-setup/for-teachers.md)! -- Jak [uruchomić kod w VSCode lub Codepace](./lessons/0-course-setup/how-to-run.md) +- Stworzyliśmy [lekcję konfiguracji](./lessons/0-course-setup/setup.md), która pomoże Ci w ustawieniu środowiska programistycznego. - Dla nauczycieli przygotowaliśmy także [lekcję konfiguracji programu nauczania](./lessons/0-course-setup/for-teachers.md)! +- Jak [uruchomić kod w VSCode lub Codespace](./lessons/0-course-setup/how-to-run.md) -Wykonaj następujące kroki: +Postępuj według tych kroków: -Forkuj repozytorium: Kliknij przycisk „Fork” w prawym górnym rogu tej strony. +Forkuj repozytorium: Kliknij przycisk "Fork" w prawym górnym rogu tej strony. Sklonuj repozytorium: `git clone https://github.com/microsoft/AI-For-Beginners.git` -Nie zapomnij dodać gwiazdki (🌟) do tego repozytorium, aby łatwiej je znaleźć później. +Nie zapomnij oznaczyć repozytorium gwiazdką (🌟), żeby łatwiej je potem znaleźć. -## Poznaj innych uczniów +## Poznaj innych uczących się -Dołącz do naszego [oficjalnego serwera Discord AI](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum), aby poznać i nawiązać kontakty z innymi uczestnikami kursu oraz uzyskać wsparcie. +Dołącz do naszego [oficjalnego serwera Discord AI](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum), aby spotkać i nawiązać kontakty z innymi uczestnikami kursu oraz uzyskać wsparcie. -Jeśli masz uwagi dotyczące produktu lub pytania podczas tworzenia, odwiedź nasz [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) +Jeśli masz opinie o produkcie lub pytania podczas tworzenia, odwiedź nasz [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) ## Quizy -> **Informacja o quizach**: Wszystkie quizy znajdują się w folderze Quiz-app w etc\quiz-app lub [dostępne online tutaj](https://ff-quizzes.netlify.app/). Są one powiązane z lekcjami, aplikację quizów można uruchomić lokalnie lub wdrożyć na Azure; postępuj zgodnie z instrukcjami w folderze `quiz-app`. Stopniowo są one lokalizowane. +> **Informacja o quizach**: Wszystkie quizy znajdują się w folderze Quiz-app w etc\quiz-app lub [online tutaj](https://ff-quizzes.netlify.app/) Są zlinkowane z lekcji; aplikacja quizowa może być uruchomiona lokalnie lub wdrożona na Azure; postępuj według instrukcji w folderze `quiz-app`. Są stopniowo lokalizowane. -## Potrzebna pomoc +## Poszukujemy pomocy -Masz sugestie lub znalazłeś błędy ortograficzne czy w kodzie? Zgłoś problem lub stwórz pull request. +Masz sugestie lub znalazłeś błędy ortograficzne lub w kodzie? Zgłoś problem lub stwórz pull request. -## Podziękowania +## Specjalne podziękowania * **✍️ Główny autor:** [Dmitry Soshnikov](http://soshnikov.com), PhD * **🔥 Redaktor:** [Jen Looper](https://twitter.com/jenlooper), PhD -* **🎨 Ilustrator notatek szkicowych:** [Tomomi Imura](https://twitter.com/girlie_mac) +* **🎨 Ilustrator sketchnotek:** [Tomomi Imura](https://twitter.com/girlie_mac) * **✅ Twórca quizów:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) * **🙏 Główni współtwórcy:** [Evgenii Pishchik](https://github.com/Pe4enIks) ## Inne programy nauczania -Nasz zespół tworzy inne programy nauczania! Sprawdź: +Nasz zespół tworzy inne programy! Sprawdź: ### LangChain @@ -185,11 +186,11 @@ Nasz zespół tworzy inne programy nauczania! Sprawdź: [![AZD dla początkujących](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst) [![Edge AI dla początkujących](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst) [![MCP dla początkujących](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst) -[![Agenci AI dla początkujących](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst) +[![AI Agenci dla początkujących](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst) --- -### Seria Generative AI +### Seria Generatywnego AI [![Generative AI dla początkujących](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst) [![Generative AI (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst) [![Generative AI (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst) @@ -198,7 +199,7 @@ Nasz zespół tworzy inne programy nauczania! Sprawdź: --- ### Podstawowe nauczanie -[![Uczenie maszynowe dla początkujących](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) +[![ML dla początkujących](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) [![Data Science dla początkujących](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst) [![AI dla początkujących](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst) [![Cyberbezpieczeństwo dla początkujących](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung) @@ -209,18 +210,18 @@ Nasz zespół tworzy inne programy nauczania! Sprawdź: --- ### Seria Copilot -[![Copilot dla AI do programowania w parze](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) +[![Copilot dla programowania w parach AI](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) [![Copilot dla C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst) [![Copilot Adventure](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst) ## Uzyskaj pomoc -Jeśli utkniesz lub masz pytania dotyczące budowania aplikacji AI, dołącz do innych uczniów i doświadczonych programistów w dyskusjach na temat MCP. To wspierająca społeczność, gdzie pytania są mile widziane, a wiedza jest swobodnie dzielona. +Jeśli utkniesz lub masz pytania dotyczące tworzenia aplikacji AI, dołącz do innych uczących się i doświadczonych programistów w dyskusjach o MCP. To wspierająca społeczność, gdzie pytania są mile widziane, a wiedza jest swobodnie dzielona. [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -Jeśli masz uwagi dotyczące produktu lub błędy podczas tworzenia, odwiedź: +Jeśli masz opinie o produkcie lub błędy podczas tworzenia, odwiedź: [![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) @@ -228,5 +229,5 @@ Jeśli masz uwagi dotyczące produktu lub błędy podczas tworzenia, odwiedź: **Zastrzeżenie**: -Niniejszy dokument został przetłumaczony przy użyciu usługi tłumaczeń AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mimo że dążymy do dokładności, prosimy mieć na uwadze, że automatyczne tłumaczenia mogą zawierać błędy lub nieścisłości. Oryginalny dokument w języku źródłowym należy traktować jako źródło wiarygodne. W przypadku informacji krytycznych zalecane jest skorzystanie z profesjonalnego tłumaczenia wykonanego przez człowieka. Nie ponosimy odpowiedzialności za jakiekolwiek nieporozumienia lub błędne interpretacje wynikające z użycia tego tłumaczenia. +Niniejszy dokument został przetłumaczony za pomocą usługi tłumaczenia AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mimo że dbamy o dokładność, prosimy mieć na uwadze, że tłumaczenia automatyczne mogą zawierać błędy lub niedokładności. Oryginalny dokument w jego języku źródłowym należy traktować jako źródło autorytatywne. W przypadku istotnych informacji zalecane jest skorzystanie z profesjonalnego tłumaczenia wykonanego przez człowieka. Nie ponosimy odpowiedzialności za jakiekolwiek nieporozumienia lub błędne interpretacje wynikające z korzystania z tego tłumaczenia. \ No newline at end of file diff --git a/translations/pl/lessons/0-course-setup/how-to-run.md b/translations/pl/lessons/0-course-setup/how-to-run.md index 85fbf70a..4ba5effd 100644 --- a/translations/pl/lessons/0-course-setup/how-to-run.md +++ b/translations/pl/lessons/0-course-setup/how-to-run.md @@ -1,21 +1,21 @@ -# Jak uruchomić kod +# Jak Uruchomić Kod -Ten kurs zawiera wiele przykładów do wykonania oraz laboratoriów, które możesz chcieć uruchomić. Aby to zrobić, musisz mieć możliwość wykonywania kodu Python w Jupyter Notebooks, które są częścią tego kursu. Masz kilka opcji uruchamiania kodu: +Ten program nauczania zawiera wiele wykonalnych przykładów i laboratoriów, które będziesz chciał uruchomić. Aby to zrobić, musisz mieć możliwość wykonywania kodu Python w Jupyter Notebooks dostarczonych w ramach tego programu nauczania. Masz kilka opcji uruchamiania kodu: -## Uruchamianie lokalnie na swoim komputerze +## Uruchamianie lokalnie na Twoim komputerze -Aby uruchomić kod lokalnie na swoim komputerze, musisz mieć zainstalowaną jakąś wersję Pythona. Osobiście polecam zainstalowanie **[miniconda](https://conda.io/en/latest/miniconda.html)** – to lekka instalacja, która obsługuje menedżera pakietów `conda` dla różnych **wirtualnych środowisk** Pythona. +Aby uruchomić kod lokalnie na Twoim komputerze, potrzebna jest instalacja Pythona. Jednym z rekomendowanych rozwiązań jest instalacja **[miniconda](https://conda.io/en/latest/miniconda.html)** - jest to raczej lekka instalacja, która wspiera menedżera pakietów `conda` do zarządzania różnymi **wirtualnymi środowiskami** Pythona. -Po zainstalowaniu minicondy musisz sklonować repozytorium i utworzyć wirtualne środowisko, które będzie używane w tym kursie: +Po zainstalowaniu minicondy, sklonuj repozytorium i utwórz wirtualne środowisko, które będzie używane na potrzeby tego kursu: ```bash git clone http://github.com/microsoft/ai-for-beginners @@ -26,51 +26,55 @@ conda activate ai4beg ### Korzystanie z Visual Studio Code z rozszerzeniem Python -Prawdopodobnie najlepszym sposobem korzystania z kursu jest otwarcie go w [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) z [rozszerzeniem Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste). +Ten program nauczania najlepiej jest używać, otwierając go w [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) z [rozszerzeniem Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste). -> **Note**: Po sklonowaniu i otwarciu katalogu w VS Code, program automatycznie zasugeruje zainstalowanie rozszerzeń Pythona. Będziesz także musiał zainstalować minicondę, jak opisano powyżej. +> **Uwaga**: Po sklonowaniu i otwarciu katalogu w VS Code, automatycznie zasugeruje Ci instalację rozszerzeń Pythona. Będziesz również musiał zainstalować minicondę, jak opisano powyżej. -> **Note**: Jeśli VS Code zasugeruje otwarcie repozytorium w kontenerze, musisz to odrzucić, aby używać lokalnej instalacji Pythona. +> **Uwaga**: Jeśli VS Code zasugeruje ponowne otwarcie repozytorium w kontenerze, powinieneś odmówić tego, aby korzystać z lokalnej instalacji Pythona. ### Korzystanie z Jupyter w przeglądarce -Możesz również korzystać ze środowiska Jupyter bezpośrednio w przeglądarce na swoim komputerze. Zarówno klasyczny Jupyter, jak i Jupyter Hub oferują wygodne środowisko programistyczne z autouzupełnianiem, podświetlaniem kodu itp. +Możesz również skorzystać ze środowiska Jupyter w przeglądarce na własnym komputerze. Zarówno klasyczny Jupyter, jak i JupyterHub oferują wygodne środowisko deweloperskie z autouzupełnianiem, podświetlaniem kodu itp. Aby uruchomić Jupyter lokalnie, przejdź do katalogu kursu i wykonaj: ```bash jupyter notebook -``` -lub +``` +lub ```bash jupyterhub -``` -Następnie możesz przejść do dowolnego pliku `.ipynb`, otworzyć go i zacząć pracę. +``` +Następnie możesz przejść do dowolnego pliku `.ipynb`, otworzyć go i rozpocząć pracę. ### Uruchamianie w kontenerze -Alternatywą dla instalacji Pythona może być uruchamianie kodu w kontenerze. Ponieważ nasze repozytorium zawiera specjalny folder `.devcontainer`, który określa, jak zbudować kontener dla tego repozytorium, VS Code zaproponuje otwarcie kodu w kontenerze. Wymaga to instalacji Dockera i jest bardziej skomplikowane, więc polecamy to bardziej zaawansowanym użytkownikom. +Alternatywą dla instalacji Pythona jest uruchomienie kodu w kontenerze. Nasze repozytorium dostarcza specjalny folder `.devcontainer`, który pokazuje, jak zbudować kontener dla tego repozytorium, a VS Code oferuje możliwość ponownego otwarcia kodu w kontenerze. Wymaga to instalacji Dockera i jest bardziej złożone, dlatego zalecamy to bardziej doświadczonym użytkownikom. -## Uruchamianie w chmurze +## Uruchamianie w Chmurze Jeśli nie chcesz instalować Pythona lokalnie i masz dostęp do zasobów w chmurze, dobrą alternatywą jest uruchamianie kodu w chmurze. Istnieje kilka sposobów, aby to zrobić: -* Korzystanie z **[GitHub Codespaces](https://github.com/features/codespaces)**, które jest wirtualnym środowiskiem utworzonym dla Ciebie na GitHubie, dostępnym przez interfejs przeglądarkowy VS Code. Jeśli masz dostęp do Codespaces, wystarczy kliknąć przycisk **Code** w repozytorium, uruchomić Codespace i zacząć pracę w mgnieniu oka. -* Korzystanie z **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. [Binder](https://mybinder.org) to darmowe zasoby obliczeniowe w chmurze, które pozwalają testować kod z GitHuba. Na stronie głównej repozytorium znajdziesz przycisk do otwarcia go w Binderze – szybko przeniesie Cię na stronę Bindera, która zbuduje kontener i uruchomi interfejs Jupyter w przeglądarce. +* Korzystanie z **[GitHub Codespaces](https://github.com/features/codespaces)** – jest to wirtualne środowisko utworzone dla Ciebie na GitHub, dostępne przez przeglądarkę VS Code. Jeśli masz dostęp do Codespaces, możesz po prostu kliknąć przycisk **Code** w repozytorium, uruchomić codespace i szybko zacząć pracę. +* Korzystanie z **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. [Binder](https://mybinder.org) oferuje darmowe zasoby obliczeniowe w chmurze, dla osób takich jak Ty, aby testować kod z GitHuba. Na stronie głównej jest przycisk umożliwiający otwarcie repozytorium w Binder – przeniesie Cię on szybko na stronę binder, która zbuduje pod spodem kontener i uruchomi interfejs Jupyter w przeglądarce bezproblemowo. -> **Note**: Aby zapobiec nadużyciom, Binder ma zablokowany dostęp do niektórych zasobów sieciowych. Może to uniemożliwić działanie kodu, który pobiera modele i/lub zestawy danych z publicznego Internetu. Może być konieczne znalezienie obejść. Ponadto zasoby obliczeniowe Bindera są dość podstawowe, więc trening będzie wolny, szczególnie w późniejszych, bardziej złożonych lekcjach. +> **Uwaga**: Aby zapobiec nadużyciom, Binder ma zablokowany dostęp do niektórych zasobów sieciowych. Może to uniemożliwić działanie niektórych fragmentów kodu, które pobierają modele i/lub zbiory danych z publicznego internetu. Może być konieczne znalezienie obejść. Dodatkowo, zasoby obliczeniowe oferowane przez Binder są dość podstawowe, więc trening będzie powolny, szczególnie w późniejszych, bardziej złożonych lekcjach. -## Uruchamianie w chmurze z obsługą GPU +## Uruchamianie w Chmurze z GPU -Niektóre z późniejszych lekcji w tym kursie znacznie skorzystają z obsługi GPU, ponieważ w przeciwnym razie trening będzie bardzo wolny. Istnieje kilka opcji, szczególnie jeśli masz dostęp do chmury, na przykład przez [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) lub swoją instytucję: +Niektóre z późniejszych lekcji w tym programie nauczania bardzo skorzystałyby na wsparciu GPU. Trening modelu, na przykład, może być w przeciwnym razie bardzo powolny. Masz kilka opcji, szczególnie jeśli masz dostęp do chmury poprzez [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) lub poprzez Twoją instytucję: -* Utwórz [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) i połącz się z nią przez Jupyter. Możesz wtedy sklonować repozytorium bezpośrednio na maszynę i rozpocząć naukę. Maszyny w serii NC mają obsługę GPU. +* Utwórz [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) i połącz się z nią przez Jupyter. Możesz wtedy sklonować repozytorium bezpośrednio na tę maszynę i zacząć naukę. Maszyny wirtualne serii NC mają wsparcie GPU. -> **Note**: Niektóre subskrypcje, w tym Azure for Students, nie oferują obsługi GPU od razu. Może być konieczne złożenie wniosku o dodatkowe rdzenie GPU przez zgłoszenie do pomocy technicznej. +> **Uwaga**: Niektóre subskrypcje, w tym Azure for Students, nie zapewniają wsparcia GPU „od ręki”. Może być konieczne zgłoszenie zapytania o dodatkowe rdzenie GPU do pomocy technicznej. -* Utwórz [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste), a następnie skorzystaj z funkcji Notebook. [Ten film](https://azure-for-academics.github.io/quickstart/azureml-papers/) pokazuje, jak sklonować repozytorium do notatnika Azure ML i zacząć z niego korzystać. +* Utwórz [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) i użyj tam funkcji Notebook. [Ten film](https://azure-for-academics.github.io/quickstart/azureml-papers/) pokazuje, jak sklonować repozytorium do notatnika Azure ML i zacząć go używać. -Możesz także skorzystać z Google Colab, który oferuje darmową obsługę GPU, i przesłać tam Jupyter Notebooks, aby wykonywać je krok po kroku. +Możesz również użyć Google Colab, który oferuje darmowe wsparcie GPU, i przesłać tam notatniki Jupyter, aby wykonywać je pojedynczo. +--- + + **Zastrzeżenie**: -Ten dokument został przetłumaczony za pomocą usługi tłumaczenia AI [Co-op Translator](https://github.com/Azure/co-op-translator). Chociaż staramy się zapewnić dokładność, prosimy mieć na uwadze, że automatyczne tłumaczenia mogą zawierać błędy lub nieścisłości. Oryginalny dokument w jego rodzimym języku powinien być uznawany za wiarygodne źródło. W przypadku informacji krytycznych zaleca się skorzystanie z profesjonalnego tłumaczenia przez człowieka. Nie ponosimy odpowiedzialności za jakiekolwiek nieporozumienia lub błędne interpretacje wynikające z użycia tego tłumaczenia. \ No newline at end of file +Niniejszy dokument został przetłumaczony za pomocą automatycznej usługi tłumaczeniowej AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mimo że staramy się zapewnić dokładność, prosimy mieć na uwadze, że tłumaczenia automatyczne mogą zawierać błędy lub niedokładności. Oryginalny dokument w języku źródłowym powinien być uznany za źródło autorytatywne. W przypadku informacji krytycznych zalecane jest skorzystanie z profesjonalnego tłumaczenia wykonanego przez człowieka. Nie ponosimy odpowiedzialności za jakiekolwiek nieporozumienia lub błędne interpretacje wynikające z korzystania z tego tłumaczenia. + \ No newline at end of file diff --git a/translations/pl/lessons/2-Symbolic/Animals.ipynb b/translations/pl/lessons/2-Symbolic/Animals.ipynb index 9e5c6778..e9a086e1 100644 --- a/translations/pl/lessons/2-Symbolic/Animals.ipynb +++ b/translations/pl/lessons/2-Symbolic/Animals.ipynb @@ -6,25 +6,25 @@ "collapsed": true }, "source": [ - "# Implementacja Eksperckiego Systemu dla Zwierząt\n", + "# Implementacja eksperckiego systemu dla zwierząt\n", "\n", - "Przykład z [AI for Beginners Curriculum](http://github.com/microsoft/ai-for-beginners).\n", + "Przykład z [Kursu AI dla początkujących](http://github.com/microsoft/ai-for-beginners).\n", "\n", - "W tym przykładzie zaimplementujemy prosty system oparty na wiedzy, który pozwala określić zwierzę na podstawie jego cech fizycznych. System można przedstawić za pomocą następującego drzewa AND-OR (to tylko część całego drzewa, łatwo możemy dodać więcej reguł):\n", + "W tym przykładzie zaimplementujemy prosty system oparty na wiedzy służący do rozpoznawania zwierząt na podstawie niektórych cech fizycznych. System można przedstawić za pomocą następującego drzewa AND-OR (to jest część całego drzewa, możemy łatwo dodać więcej reguł):\n", "\n", - "![](../../../../lessons/2-Symbolic/images/AND-OR-Tree.png)\n" + "![](../../../../../../translated_images/pl/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Nasza własna powłoka systemów eksperckich z wnioskowaniem wstecznym\n", + "## Nasza własna powłoka eksperckich systemów z wnioskowaniem wstecznym\n", "\n", - "Spróbujmy zdefiniować prosty język do reprezentacji wiedzy oparty na regułach produkcji. Użyjemy klas Pythona jako słów kluczowych do definiowania reguł. Będą zasadniczo 3 typy klas:\n", - "* `Ask` reprezentuje pytanie, które należy zadać użytkownikowi. Zawiera zestaw możliwych odpowiedzi.\n", - "* `If` reprezentuje regułę i jest jedynie syntaktycznym ułatwieniem do przechowywania treści reguły.\n", - "* `AND`/`OR` to klasy reprezentujące gałęzie AND/OR drzewa. Przechowują jedynie listę argumentów w środku. Aby uprościć kod, cała funkcjonalność jest zdefiniowana w klasie nadrzędnej `Content`.\n" + "Spróbujmy zdefiniować prosty język do reprezentacji wiedzy oparty na regułach produkcyjnych. Użyjemy klas Pythona jako słów kluczowych do definiowania reguł. W zasadzie będą trzy typy klas:\n", + "* `Ask` reprezentuje pytanie, które należy zadać użytkownikowi. Zawiera zbiór możliwych odpowiedzi.\n", + "* `If` reprezentuje regułę, i jest to tylko składniowy cukier do przechowywania treści reguły\n", + "* `AND`/`OR` to klasy reprezentujące gałęzie drzewa typu AND/OR. Przechowują one listę argumentów wewnątrz. Aby uprościć kod, cała funkcjonalność jest zdefiniowana w klasie bazowej `Content`\n" ] }, { @@ -66,7 +66,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "W naszym systemie pamięć robocza zawierałaby listę **faktów** jako **pary atrybut-wartość**. Bazę wiedzy można zdefiniować jako jeden duży słownik, który mapuje działania (nowe fakty, które powinny zostać wprowadzone do pamięci roboczej) na warunki, wyrażone jako wyrażenia AND-OR. Ponadto niektóre fakty mogą być `Zapytane`.\n" + "W naszym systemie pamięć robocza zawierałaby listę **faktów** jako **pary atrybut-wartość**. Bazę wiedzy można zdefiniować jako jeden duży słownik, który mapuje akcje (nowe fakty, które powinny zostać wstawione do pamięci roboczej) na warunki, wyrażone jako wyrażenia AND-OR. Ponadto niektóre fakty mogą być `Ask`-owane.\n" ] }, { @@ -99,13 +99,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Aby przeprowadzić wnioskowanie wsteczne, zdefiniujemy klasę `Knowledgebase`. Będzie ona zawierać:\n", - "* `Pamięć roboczą` - słownik, który mapuje atrybuty na wartości\n", - "* `Zasady bazy wiedzy` w formacie zdefiniowanym powyżej\n", + "Aby wykonać wnioskowanie wsteczne, zdefiniujemy klasę `Knowledgebase`. Będzie ona zawierać:\n", + "* Roboczą `memory` - słownik mapujący atrybuty na wartości\n", + "* Zasady bazy wiedzy `rules` w formacie zdefiniowanym powyżej\n", "\n", - "Dwa główne metody to:\n", - "* `get`, aby uzyskać wartość atrybutu, wykonując w razie potrzeby wnioskowanie. Na przykład, `get('color')` pobierze wartość dla slotu koloru (jeśli zajdzie potrzeba, zapyta o wartość i zapisze ją do późniejszego użycia w pamięci roboczej). Jeśli zapytamy `get('color:blue')`, zapyta o kolor, a następnie zwróci wartość `y`/`n` w zależności od koloru.\n", - "* `eval` wykonuje rzeczywiste wnioskowanie, czyli przechodzi przez drzewo AND/OR, ocenia podcele, itd.\n" + "Dwie główne metody to:\n", + "* `get` do uzyskania wartości atrybutu, wykonująca wnioskowanie w razie potrzeby. Na przykład, `get('color')` pobierze wartość pola koloru (zapytuje, jeśli trzeba, i zapisuje wartość do późniejszego użycia w pamięci roboczej). Jeśli zapytamy o `get('color:blue')`, zapyta o kolor, a następnie zwróci wartość `y`/`n` w zależności od koloru.\n", + "* `eval` wykonuje faktyczne wnioskowanie, tzn. przeszukuje drzewo AND/OR, ocenia podcele itd.\n" ] }, { @@ -172,7 +172,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Teraz zdefiniujmy naszą bazę wiedzy o zwierzętach i przeprowadźmy konsultację. Zauważ, że to wywołanie będzie zadawać Ci pytania. Możesz odpowiadać, wpisując `y`/`n` na pytania tak/nie lub podając liczbę (0..N) na pytania z dłuższymi odpowiedziami wielokrotnego wyboru.\n" + "Teraz zdefiniujmy naszą bazę wiedzy o zwierzętach i przeprowadźmy konsultację. Zauważ, że to wywołanie będzie zadawać pytania. Możesz odpowiadać, wpisując `y`/`n` dla pytań tak/nie lub podając numer (0..N) dla pytań z dłuższymi odpowiedziami do wyboru.\n" ] }, { @@ -229,11 +229,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Korzystanie z PyKnow do wnioskowania w przód\n", + "## Korzystanie z Experta do wnioskowania progresywnego\n", "\n", - "W poniższym przykładzie spróbujemy zaimplementować wnioskowanie w przód, korzystając z jednej z bibliotek do reprezentacji wiedzy, [PyKnow](https://github.com/buguroo/pyknow/). **PyKnow** to biblioteka do tworzenia systemów wnioskowania w przód w Pythonie, zaprojektowana w sposób przypominający klasyczny, starszy system [CLIPS](http://www.clipsrules.net/index.html).\n", + "W następnym przykładzie spróbujemy zaimplementować wnioskowanie progresywne, korzystając z jednej z bibliotek do reprezentacji wiedzy, [Experta](https://github.com/nilp0inter/experta). **Experta** to biblioteka do tworzenia systemów wnioskowania progresywnego w Pythonie, zaprojektowana tak, aby była podobna do klasycznego, starego systemu [CLIPS](http://www.clipsrules.net/index.html).\n", "\n", - "Moglibyśmy również zaimplementować wnioskowanie w przód samodzielnie bez większych trudności, ale naiwne implementacje zazwyczaj nie są zbyt wydajne. Do bardziej efektywnego dopasowywania reguł używa się specjalnego algorytmu [Rete](https://en.wikipedia.org/wiki/Rete_algorithm).\n" + "Moglibyśmy także sami zaimplementować wnioskowanie progresywne bez większych problemów, ale naiwne implementacje zazwyczaj nie są zbyt efektywne. Do bardziej efektywnego dopasowywania reguł używany jest specjalny algorytm [Rete](https://en.wikipedia.org/wiki/Rete_algorithm).\n" ] }, { @@ -247,32 +247,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "Collecting git+https://github.com/buguroo/pyknow/\n", - " Cloning https://github.com/buguroo/pyknow/ to /tmp/pip-req-build-3cqeulyl\n", - " Running command git clone --filter=blob:none --quiet https://github.com/buguroo/pyknow/ /tmp/pip-req-build-3cqeulyl\n", - " Resolved https://github.com/buguroo/pyknow/ to commit 48818336f2e9a126f1964f2d8dc22d37ff800fe8\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting frozendict==1.2\n", - " Using cached frozendict-1.2.tar.gz (2.6 kB)\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting schema==0.6.7\n", - " Using cached schema-0.6.7-py2.py3-none-any.whl (14 kB)\n", - "Building wheels for collected packages: pyknow, frozendict\n", - " Building wheel for pyknow (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for pyknow: filename=pyknow-1.7.0-py3-none-any.whl size=34228 sha256=b7de5b09292c4007667c72f69b98d5a1b5f7324ff15f9dd8e077c3d5f7aade42\n", - " Stored in directory: /tmp/pip-ephem-wheel-cache-k7jpave7/wheels/81/1a/d3/f6c15dbe1955598a37755215f2a10449e7418500d7bd4b9508\n", - " Building wheel for frozendict (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for frozendict: filename=frozendict-1.2-py3-none-any.whl size=3148 sha256=2863d55c240d2409cddf05ccfe600591f8478681549fc97555c47c90dc6bb160\n", - " Stored in directory: /home/rg/.cache/pip/wheels/49/ac/f8/cb8120244e710bdb479c86198b03c7b08c3c2d3d2bf448fd6e\n", - "Successfully built pyknow frozendict\n", - "Installing collected packages: schema, frozendict, pyknow\n", - "Successfully installed frozendict-1.2 pyknow-1.7.0 schema-0.6.7\n" + "Collecting git+https://github.com/nilp0inter/experta\n", + " Cloning https://github.com/nilp0inter/experta to /tmp/pip-req-build-7qurtwk3\n", + " Running command git clone --filter=blob:none --quiet https://github.com/nilp0inter/experta /tmp/pip-req-build-7qurtwk3\n", + " Resolved https://github.com/nilp0inter/experta to commit c6d5834b123861f5ae09e7d07027dc98bec58741\n", + " Installing build dependencies ... \u001b[?25ldone\n", + "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", + "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25hRequirement already satisfied: frozendict~=2.4.6 in /opt/conda/envs/ai4beg/lib/python3.12/site-packages (from experta==1.9.5.dev1) (2.4.7)\n", + "Collecting schema~=0.6.7 (from experta==1.9.5.dev1)\n", + " Downloading schema-0.6.8-py2.py3-none-any.whl.metadata (14 kB)\n", + "Downloading schema-0.6.8-py2.py3-none-any.whl (14 kB)\n", + "Building wheels for collected packages: experta\n", + " Building wheel for experta (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25h Created wheel for experta: filename=experta-1.9.5.dev1-py3-none-any.whl size=34804 sha256=888c459512a5e713f4b674caa9a0f96cfdf07ec0d6eb56cc318ce0653d218014\n", + " Stored in directory: /tmp/pip-ephem-wheel-cache-1eeii9zy/wheels/3d/e8/bb/22d7956359603fa8dd679aa09f5b8efb3f29991c3986fdc787\n", + "Successfully built experta\n", + "Installing collected packages: schema, experta\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2/2\u001b[0m [experta]\n", + "\u001b[1A\u001b[2KSuccessfully installed experta-1.9.5.dev1 schema-0.6.8\n" ] } ], "source": [ "import sys\n", - "!{sys.executable} -m pip install git+https://github.com/buguroo/pyknow/" + "!{sys.executable} -m pip install git+https://github.com/nilp0inter/experta" ] }, { @@ -283,15 +282,15 @@ }, "outputs": [], "source": [ - "from pyknow import *\n", - "#import pyknow" + "from experta import *\n", + "#import experta" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Zdefiniujemy nasz system jako klasę, która dziedziczy po `KnowledgeEngine`. Każda reguła jest definiowana przez osobną funkcję z adnotacją `@Rule`, która określa, kiedy reguła powinna zostać uruchomiona. Wewnątrz reguły możemy dodawać nowe fakty za pomocą funkcji `declare`, a dodanie tych faktów spowoduje wywołanie kolejnych reguł przez silnik wnioskowania w przód.\n" + "Zdefiniujemy nasz system jako klasę dziedziczącą po `KnowledgeEngine`. Każda reguła jest definiowana przez osobną funkcję z adnotacją `@Rule`, która określa, kiedy reguła powinna zostać wywołana. Wewnątrz reguły możemy dodać nowe fakty za pomocą funkcji `declare`, a dodanie tych faktów spowoduje wywołanie kolejnych reguł przez silnik wnioskowania w przód.\n" ] }, { @@ -378,7 +377,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Gdy zdefiniujemy bazę wiedzy, wypełniamy naszą pamięć roboczą początkowymi faktami, a następnie wywołujemy metodę `run()`, aby przeprowadzić wnioskowanie. W rezultacie można zauważyć, że nowe wywnioskowane fakty są dodawane do pamięci roboczej, w tym ostateczny fakt dotyczący zwierzęcia (jeśli poprawnie ustawimy wszystkie początkowe fakty).\n" + "Gdy zdefiniujemy bazę wiedzy, wprowadzamy do naszej pamięci roboczej kilka początkowych faktów, a następnie wywołujemy metodę `run()`, aby przeprowadzić wnioskowanie. W efekcie można zauważyć, że nowe wnioskowane fakty są dodawane do pamięci roboczej, w tym ostateczny fakt dotyczący zwierzęcia (jeśli poprawnie ustawiliśmy wszystkie początkowe fakty).\n" ] }, { @@ -440,7 +439,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n---\n\n**Zastrzeżenie**: \nTen dokument został przetłumaczony za pomocą usługi tłumaczenia AI [Co-op Translator](https://github.com/Azure/co-op-translator). Chociaż dokładamy wszelkich starań, aby zapewnić poprawność tłumaczenia, prosimy pamiętać, że automatyczne tłumaczenia mogą zawierać błędy lub nieścisłości. Oryginalny dokument w jego rodzimym języku powinien być uznawany za wiarygodne źródło. W przypadku informacji o kluczowym znaczeniu zaleca się skorzystanie z profesjonalnego tłumaczenia przez człowieka. Nie ponosimy odpowiedzialności za jakiekolwiek nieporozumienia lub błędne interpretacje wynikające z użycia tego tłumaczenia.\n" + "---\n\n\n**Zastrzeżenie**: \nTen dokument został przetłumaczony za pomocą usługi tłumaczenia AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mimo że staramy się zapewnić dokładność, prosimy pamiętać, że automatyczne tłumaczenia mogą zawierać błędy lub niedokładności. Oryginalny dokument w języku źródłowym powinien być uznawany za autorytatywne źródło. W przypadku informacji o krytycznym znaczeniu zalecane jest skorzystanie z profesjonalnego tłumaczenia wykonanego przez człowieka. Nie ponosimy odpowiedzialności za jakiekolwiek nieporozumienia lub błędne interpretacje wynikające z użycia tego tłumaczenia.\n\n" ] } ], @@ -467,8 +466,8 @@ "version": "3.11.2" }, "coopTranslator": { - "original_hash": "ab2bd97b0453415b89a469284609a8ce", - "translation_date": "2025-08-31T13:16:30+00:00", + "original_hash": "8ef43db4b9182239fd150a76bd494fdb", + "translation_date": "2026-01-15T14:04:46+00:00", "source_file": "lessons/2-Symbolic/Animals.ipynb", "language_code": "pl" } diff --git a/translations/pl/lessons/2-Symbolic/README.md b/translations/pl/lessons/2-Symbolic/README.md index ee8ac934..3e939917 100644 --- a/translations/pl/lessons/2-Symbolic/README.md +++ b/translations/pl/lessons/2-Symbolic/README.md @@ -1,75 +1,75 @@ -# Reprezentacja wiedzy i systemy ekspertowe +# Reprezentacja Wiedzy i Systemy Eksperckie -![Podsumowanie treści o Symbolicznym AI](../../../../translated_images/pl/ai-symbolic.715a30cb610411a6.webp) +![Podsumowanie treści Symbolicznej AI](../../../../../../translated_images/pl/ai-symbolic.715a30cb610411a6.webp) > Sketchnote autorstwa [Tomomi Imura](https://twitter.com/girlie_mac) -Dążenie do sztucznej inteligencji opiera się na poszukiwaniu wiedzy, aby zrozumieć świat w sposób podobny do ludzi. Ale jak można to osiągnąć? +Poszukiwanie sztucznej inteligencji opiera się na dążeniu do wiedzy, aby rozumieć świat podobnie jak ludzie. Ale jak można to osiągnąć? ## [Quiz przed wykładem](https://ff-quizzes.netlify.app/en/ai/quiz/3) -W początkowych dniach rozwoju AI popularne było podejście odgórne do tworzenia inteligentnych systemów (omówione w poprzedniej lekcji). Polegało ono na wydobywaniu wiedzy od ludzi w formie zrozumiałej dla maszyn, a następnie wykorzystywaniu jej do automatycznego rozwiązywania problemów. Podejście to opierało się na dwóch kluczowych ideach: +Na początku rozwoju AI popularne było podejście odgórne do tworzenia inteligentnych systemów (omówione w poprzedniej lekcji). Chodziło o wydobycie wiedzy od ludzi do formy czytelnej dla maszyn, a następnie użycie jej do automatycznego rozwiązywania problemów. To podejście opierało się na dwóch głównych ideach: * Reprezentacja wiedzy * Wnioskowanie ## Reprezentacja wiedzy -Jednym z ważnych pojęć w Symbolicznym AI jest **wiedza**. Ważne jest, aby odróżnić wiedzę od *informacji* czy *danych*. Na przykład można powiedzieć, że książki zawierają wiedzę, ponieważ można je studiować i stać się ekspertem. Jednak to, co zawierają książki, to właściwie *dane*, a czytając książki i integrując te dane z naszym modelem świata, przekształcamy je w wiedzę. +Jednym z ważnych pojęć w Symbolicznej AI jest **wiedza**. Ważne jest, aby odróżnić wiedzę od *informacji* lub *danych*. Na przykład można powiedzieć, że książki zawierają wiedzę, ponieważ można studiować książki i stać się ekspertem. Jednak to, co książki zawierają, to w rzeczywistości *dane*, a przez czytanie książek i integrację tych danych z naszym modelem świata przekształcamy dane w wiedzę. -> ✅ **Wiedza** to coś, co znajduje się w naszej głowie i reprezentuje nasze rozumienie świata. Jest zdobywana poprzez aktywny proces **uczenia się**, który integruje otrzymywane informacje z naszym aktywnym modelem świata. +> ✅ **Wiedza** to coś, co jest zawarte w naszej głowie i reprezentuje nasze rozumienie świata. Otrzymujemy ją poprzez aktywny proces **uczenia się**, który integruje kawałki informacji, które otrzymujemy, z naszym aktywnym modelem świata. -Najczęściej nie definiujemy wiedzy w sposób ścisły, ale zestawiamy ją z innymi powiązanymi pojęciami za pomocą [Piramidy DIKW](https://en.wikipedia.org/wiki/DIKW_pyramid). Zawiera ona następujące poziomy: +Najczęściej nie definiujemy wiedzy ściśle, ale wyrównujemy ją z innymi pokrewnymi pojęciami za pomocą [piramidy DIKW](https://en.wikipedia.org/wiki/DIKW_pyramid). Zawiera ona następujące pojęcia: -* **Dane** to coś reprezentowanego na nośnikach fizycznych, takich jak tekst pisany czy słowa mówione. Dane istnieją niezależnie od ludzi i mogą być przekazywane między nimi. -* **Informacja** to sposób, w jaki interpretujemy dane w naszej głowie. Na przykład, gdy słyszymy słowo *komputer*, mamy pewne wyobrażenie, czym ono jest. -* **Wiedza** to informacja zintegrowana z naszym modelem świata. Na przykład, gdy nauczymy się, czym jest komputer, zaczynamy mieć pewne pojęcie o tym, jak działa, ile kosztuje i do czego można go używać. Ta sieć powiązanych pojęć tworzy naszą wiedzę. -* **Mądrość** to jeszcze wyższy poziom rozumienia świata, reprezentujący *meta-wiedzę*, np. wiedzę o tym, jak i kiedy należy używać wiedzy. +* **Dane** to coś reprezentowanego w nośnikach fizycznych, takich jak tekst pisany lub słowa mówione. Dane istnieją niezależnie od ludzi i mogą być przekazywane między ludźmi. +* **Informacja** to sposób, w jaki interpretujemy dane w naszej głowie. Na przykład, kiedy słyszymy słowo *komputer*, mamy pewne pojęcie, czym on jest. +* **Wiedza** to informacja zintegrowana z naszym modelem świata. Na przykład, gdy nauczymy się, co to jest komputer, zaczynamy mieć pewne wyobrażenia o tym, jak działa, ile kosztuje i do czego można go używać. Ta sieć powiązanych pojęć tworzy naszą wiedzę. +* **Mądrość** to jeszcze jeden poziom naszego rozumienia świata i reprezentuje *meta-wiedzę*, np. pewne pojęcia o tym, jak i kiedy wiedza powinna być używana. - + -*Obraz [z Wikipedii](https://commons.wikimedia.org/w/index.php?curid=37705247), autorstwa Longlivetheux - własne dzieło, CC BY-SA 4.0* +*Obraz [z Wikipedii](https://commons.wikimedia.org/w/index.php?curid=37705247), autor Longlivetheux - własna praca, na licencji CC BY-SA 4.0* -Problem **reprezentacji wiedzy** polega więc na znalezieniu skutecznego sposobu reprezentowania wiedzy w komputerze w formie danych, aby była automatycznie użyteczna. Można to postrzegać jako spektrum: +Problematyka **reprezentacji wiedzy** polega zatem na znalezieniu skutecznego sposobu przedstawienia wiedzy w komputerze w formie danych, aby mogła być automatycznie wykorzystywana. Można to zobaczyć jako spektrum: -![Spektrum reprezentacji wiedzy](../../../../translated_images/pl/knowledge-spectrum.b60df631852c0217.webp) +![Spektrum reprezentacji wiedzy](../../../../../../translated_images/pl/knowledge-spectrum.b60df631852c0217.webp) > Obraz autorstwa [Dmitry Soshnikov](http://soshnikov.com) -* Po lewej stronie znajdują się bardzo proste typy reprezentacji wiedzy, które mogą być skutecznie wykorzystywane przez komputery. Najprostszą formą jest algorytmiczna, gdzie wiedza jest reprezentowana przez program komputerowy. Nie jest to jednak najlepszy sposób reprezentowania wiedzy, ponieważ jest mało elastyczny. Wiedza w naszej głowie często nie ma charakteru algorytmicznego. -* Po prawej stronie znajdują się reprezentacje takie jak naturalny tekst. Jest to najbardziej potężna forma, ale nie może być używana do automatycznego wnioskowania. +* Po lewej stronie znajdują się bardzo proste typy reprezentacji wiedzy, które mogą być efektywnie używane przez komputery. Najprostszy to algorytmiczny, gdy wiedza jest reprezentowana przez program komputerowy. Nie jest to jednak najlepszy sposób reprezentacji wiedzy, ponieważ nie jest elastyczny. Wiedza w naszej głowie często nie jest algorytmiczna. +* Po prawej stronie są reprezentacje takie jak tekst naturalny. Jest on najsilniejszy, ale nie może być używany do automatycznego wnioskowania. -> ✅ Zastanów się przez chwilę, jak reprezentujesz wiedzę w swojej głowie i przekształcasz ją w notatki. Czy istnieje jakiś format, który dobrze wspomaga zapamiętywanie? +> ✅ Pomyśl przez moment o tym, jak reprezentujesz wiedzę w swojej głowie i przekształcasz ją w notatki. Czy istnieje jakiś konkretny format, który dobrze Ci służy, pomagając w zapamiętywaniu? ## Klasyfikacja reprezentacji wiedzy w komputerach -Możemy sklasyfikować różne metody reprezentacji wiedzy w komputerach w następujące kategorie: +Możemy klasyfikować różne metody komputerowej reprezentacji wiedzy w następujące kategorie: -* **Reprezentacje sieciowe** opierają się na fakcie, że w naszej głowie mamy sieć powiązanych pojęć. Możemy próbować odtworzyć te same sieci jako graf w komputerze - tzw. **sieć semantyczną**. +* **Reprezentacje sieciowe** opierają się na fakcie, że mamy sieć powiązanych ze sobą pojęć w naszej głowie. Możemy spróbować odtworzyć takie same sieci jako graf w komputerze - tzw. **sieć semantyczną**. -1. **Trójki obiekt-atrybut-wartość** lub **pary atrybut-wartość**. Ponieważ graf może być reprezentowany w komputerze jako lista węzłów i krawędzi, możemy reprezentować sieć semantyczną jako listę trójek zawierających obiekty, atrybuty i wartości. Na przykład, budujemy następujące trójki o językach programowania: +1. **Trójki obiekt-atrybut-wartość** lub **pary atrybut-wartość**. Ponieważ graf może być reprezentowany w komputerze jako lista węzłów i krawędzi, możemy przedstawić sieć semantyczną jako listę trójek, zawierających obiekty, atrybuty i wartości. Na przykład budujemy następujące trójki dotyczące języków programowania: Obiekt | Atrybut | Wartość -------|---------|-------- -Python | jest | Językiem nietypowanym -Python | wynaleziony-przez | Guido van Rossum -Python | składnia-blokowa | wcięcia -Język nietypowany | nie ma | definicji typów +Python | jest | językiem nie typowanym +Python | wynalazł | Guido van Rossum +Python | składnia bloku | wcięcia +język nie typowany | nie ma | definicji typów -> ✅ Zastanów się, jak trójki mogą być używane do reprezentowania innych typów wiedzy. +> ✅ Pomyśl, jak trójki mogą być użyte do reprezentacji innych typów wiedzy. -2. **Reprezentacje hierarchiczne** podkreślają fakt, że często tworzymy hierarchię obiektów w naszej głowie. Na przykład, wiemy, że kanarek to ptak, a wszystkie ptaki mają skrzydła. Mamy też pewne wyobrażenie o tym, jaki kolor ma kanarek i jaka jest jego prędkość lotu. +2. **Reprezentacje hierarchiczne** podkreślają fakt, że często tworzymy w hierarchię obiektów w naszej głowie. Na przykład wiemy, że kanarek to ptak, a wszystkie ptaki mają skrzydła. Mamy także pewne pojęcie o kolorze kanarka i prędkości jego lotu. - - **Reprezentacja ramowa** opiera się na reprezentowaniu każdego obiektu lub klasy obiektów jako **ramki**, która zawiera **sloty**. Sloty mają możliwe wartości domyślne, ograniczenia wartości lub procedury przechowywane, które można wywołać, aby uzyskać wartość slotu. Wszystkie ramki tworzą hierarchię podobną do hierarchii obiektów w językach programowania obiektowego. + - **Reprezentacja ramowa** opiera się na reprezentowaniu każdego obiektu lub klasy obiektów jako **ramki**, która zawiera **sloty**. Sloty mają możliwe domyślne wartości, ograniczenia wartości lub przechowywane procedury, które można wywołać, by uzyskać wartość slotu. Wszystkie ramki tworzą hierarchię podobną do hierarchii obiektów w programowaniu obiektowym. - **Scenariusze** to specjalny rodzaj ramek, które reprezentują złożone sytuacje rozwijające się w czasie. **Python** @@ -77,40 +77,40 @@ Język nietypowany | nie ma | definicji typów Slot | Wartość | Wartość domyślna | Przedział | -----|---------|------------------|-----------| Nazwa | Python | | | -Jest-A | Język nietypowany | | | -Styl zmiennych | | CamelCase | | +Jest | Język nie typowany | | | +Styl zapisu zmiennych | | CamelCase | | Długość programu | | | 5-5000 linii | -Składnia blokowa | Wcięcia | | | +Składnia bloku | Wcięcie | | | -3. **Reprezentacje proceduralne** opierają się na reprezentowaniu wiedzy jako listy działań, które można wykonać, gdy wystąpi określony warunek. - - Reguły produkcji to instrukcje if-then, które pozwalają nam wyciągać wnioski. Na przykład, lekarz może mieć regułę mówiącą, że **JEŚLI** pacjent ma wysoką gorączkę **LUB** wysoki poziom białka C-reaktywnego w badaniu krwi **TO** ma stan zapalny. Gdy napotkamy jeden z warunków, możemy wyciągnąć wniosek o stanie zapalnym, a następnie użyć go w dalszym wnioskowaniu. - - Algorytmy można uznać za inną formę reprezentacji proceduralnej, choć prawie nigdy nie są używane bezpośrednio w systemach opartych na wiedzy. +3. **Reprezentacje proceduralne** opierają się na reprezentowaniu wiedzy jako listy działań, które mogą zostać wykonane, gdy zajdzie określony warunek. + - Reguły produkcji to instrukcje typu jeśli-to, które pozwalają nam wyciągać wnioski. Na przykład lekarz może mieć regułę mówiącą, że **JEŚLI** pacjent ma wysoką gorączkę **LUB** wysoki poziom białka C-reaktywnego w badaniu krwi, **TO** ma zapalenie. Po spełnieniu jednego z warunków możemy wyciągnąć wniosek o zapaleniu i wykorzystać go w dalszym wnioskowaniu. + - Algorytmy można również uznać za formę reprezentacji proceduralnej, chociaż prawie nigdy nie są one używane bezpośrednio w systemach opartych na wiedzy. -4. **Logika** została pierwotnie zaproponowana przez Arystotelesa jako sposób reprezentowania uniwersalnej ludzkiej wiedzy. - - Logika predykatów jako teoria matematyczna jest zbyt bogata, aby była obliczalna, dlatego zwykle używa się jej podzbioru, takiego jak klauzule Horn używane w Prologu. - - Logika deskryptywna to rodzina systemów logicznych używanych do reprezentowania i wnioskowania o hierarchiach obiektów w rozproszonych reprezentacjach wiedzy, takich jak *sieć semantyczna*. +4. **Logika** została pierwotnie zaproponowana przez Arystotelesa jako sposób reprezentacji uniwersalnej wiedzy ludzkiej. + - Logika predykatów jako teoria matematyczna jest zbyt bogata, by była obliczalna, dlatego zwykle używa się jej podzbioru, jak np. klauzule Horna używane w Prologu. + - Logika opisowa (Descriptive Logic) to rodzina systemów logicznych używanych do reprezentacji i wnioskowania o hierarchiach obiektów oraz rozproszonych reprezentacjach wiedzy, takich jak *semantyczny web*. -## Systemy ekspertowe +## Systemy eksperckie -Jednym z wczesnych sukcesów symbolicznego AI były tzw. **systemy ekspertowe** - systemy komputerowe zaprojektowane do działania jako ekspert w ograniczonej dziedzinie problemowej. Opierały się na **bazie wiedzy** wydobytej od jednego lub więcej ludzkich ekspertów i zawierały **silnik wnioskowania**, który wykonywał wnioskowanie na jej podstawie. +Jednym z wczesnych sukcesów symbolicznej AI były tzw. **systemy eksperckie** - systemy komputerowe zaprojektowane do działania jako ekspert w ograniczonym obszarze problemowym. Opierały się na **bazie wiedzy** wydobytej od jednego lub więcej ekspertów oraz zawierały **silnik wnioskowania**, który wykonywał pewne rozumowania na jej podstawie. -![Architektura człowieka](../../../../translated_images/pl/arch-human.5d4d35f1bba3ab1c.webp) | ![System oparty na wiedzy](../../../../translated_images/pl/arch-kbs.3ec5c150b09fa8da.webp) +![Architektura ludzka](../../../../../../translated_images/pl/arch-human.5d4d35f1bba3ab1c.webp) | ![System oparty na wiedzy](../../../../../../translated_images/pl/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ -Uproszczona struktura ludzkiego układu nerwowego | Architektura systemu opartego na wiedzy +Uproszczona struktura ludzkiego systemu nerwowego | Architektura systemu opartego na wiedzy -Systemy ekspertowe są zbudowane podobnie jak system wnioskowania człowieka, który zawiera **pamięć krótkotrwałą** i **pamięć długotrwałą**. Podobnie w systemach opartych na wiedzy wyróżniamy następujące komponenty: +Systemy eksperckie są budowane na wzór ludzkiego systemu rozumowania, który zawiera **pamięć krótkotrwałą** oraz **pamięć długotrwałą**. Podobnie w systemach opartych na wiedzy wyróżniamy następujące komponenty: -* **Pamięć problemu**: zawiera wiedzę o aktualnie rozwiązywanym problemie, np. temperaturę czy ciśnienie krwi pacjenta, czy ma stan zapalny, itd. Ta wiedza nazywana jest również **wiedzą statyczną**, ponieważ zawiera migawkę tego, co obecnie wiemy o problemie - tzw. *stan problemu*. -* **Baza wiedzy**: reprezentuje długoterminową wiedzę o dziedzinie problemowej. Jest ręcznie wydobywana od ludzkich ekspertów i nie zmienia się od konsultacji do konsultacji. Ponieważ pozwala nawigować od jednego stanu problemu do drugiego, nazywana jest również **wiedzą dynamiczną**. -* **Silnik wnioskowania**: organizuje cały proces przeszukiwania przestrzeni stanów problemu, zadając pytania użytkownikowi, gdy jest to konieczne. Odpowiada również za znajdowanie odpowiednich reguł do zastosowania w każdym stanie. +* **Pamięć problemu**: zawiera wiedzę o problemie aktualnie rozwiązywanym, np. temperaturę lub ciśnienie krwi pacjenta, czy ma zapalenie czy nie itd. Ta wiedza jest również nazywana **wiedzą statyczną**, ponieważ zawiera migawkę tego, co aktualnie wiemy o problemie – tzw. *stan problemu*. +* **Baza wiedzy**: reprezentuje wiedzę długotrwałą o dziedzinie problemu. Jest ręcznie wydobywana od ekspertów i nie zmienia się podczas kolejnych konsultacji. Ponieważ umożliwia przejście z jednego stanu problemu do drugiego, nazywana jest także **wiedzą dynamiczną**. +* **Silnik wnioskowania**: koordynuje cały proces przeszukiwania przestrzeni stanów problemu, zadaje pytania użytkownikowi w razie potrzeby. Odpowiada również za wybór odpowiednich reguł, które zostaną zastosowane w każdym stanie. -Na przykład rozważmy następujący system ekspertowy do określania zwierzęcia na podstawie jego cech fizycznych: +Na przykład rozważmy system ekspercki do identyfikacji zwierzęcia na podstawie jego cech fizycznych: -![Drzewo AND-OR](../../../../translated_images/pl/AND-OR-Tree.5592d2c70187f283.webp) +![Drzewo AND-OR](../../../../../../translated_images/pl/AND-OR-Tree.5592d2c70187f283.webp) > Obraz autorstwa [Dmitry Soshnikov](http://soshnikov.com) -Ten diagram nazywa się **drzewem AND-OR** i jest graficzną reprezentacją zestawu reguł produkcji. Rysowanie drzewa jest przydatne na początku wydobywania wiedzy od eksperta. Aby reprezentować wiedzę w komputerze, wygodniej jest używać reguł: +Ten diagram nazywa się **drzewem AND-OR** i jest graficzna reprezentacją zestawu reguł produkcji. Rysowanie drzewa jest przydatne na początku wydobywania wiedzy od eksperta. Do reprezentacji wiedzy wewnątrz komputera wygodniej jest używać reguł: ``` IF the animal eats meat @@ -121,78 +121,78 @@ OR (animal has sharp teeth THEN the animal is a carnivore ``` -Możesz zauważyć, że każdy warunek po lewej stronie reguły i akcja są w zasadzie trójkami obiekt-atrybut-wartość (OAV). **Pamięć robocza** zawiera zestaw trójek OAV odpowiadających aktualnie rozwiązywanemu problemowi. **Silnik reguł** szuka reguł, dla których warunek jest spełniony, i stosuje je, dodając kolejną trójkę do pamięci roboczej. +Możesz zauważyć, że każdy warunek po lewej stronie reguły oraz akcja to właściwie trójki obiekt-atrybut-wartość (OAV). **Pamięć robocza** zawiera zestaw trójek OAV odpowiadających aktualnie rozwiązywanemu problemowi. **Silnik reguł** szuka reguł, których warunek jest spełniony i je stosuje, dodając kolejną trójkę do pamięci roboczej. -> ✅ Narysuj własne drzewo AND-OR na temat, który Cię interesuje! +> ✅ Napisz swoje własne drzewo AND-OR na dowolny temat, który lubisz! -### Wnioskowanie w przód vs. wnioskowanie wstecz +### Wnioskowanie w przód vs. wnioskowanie w tył -Proces opisany powyżej nazywa się **wnioskowaniem w przód**. Zaczyna się od pewnych początkowych danych o problemie dostępnych w pamięci roboczej, a następnie wykonuje następującą pętlę wnioskowania: +Opisany powyżej proces nazywa się **wnioskowaniem w przód**. Zaczyna się od pewnych danych początkowych o problemie dostępnych w pamięci roboczej, a następnie wykonuje następującą pętlę rozumowania: -1. Jeśli docelowy atrybut jest obecny w pamięci roboczej - zatrzymaj się i podaj wynik -2. Poszukaj wszystkich reguł, których warunek jest obecnie spełniony - uzyskaj **zestaw konfliktów** reguł. -3. Wykonaj **rozwiązanie konfliktu** - wybierz jedną regułę, która zostanie wykonana w tym kroku. Mogą istnieć różne strategie rozwiązywania konfliktów: +1. Jeśli atrybut docelowy jest obecny w pamięci roboczej – zatrzymaj się i podaj wynik +2. Szukaj wszystkich reguł, których warunek jest obecnie spełniony – otrzymaj **zbiór konfliktu** reguł +3. Wykonaj **rozstrzyganie konfliktów** – wybierz jedną regułę, która zostanie wykonana na tym kroku. Mogą być różne strategie rozstrzygania konfliktów: - Wybierz pierwszą pasującą regułę w bazie wiedzy - Wybierz losową regułę - - Wybierz *bardziej szczegółową* regułę, tj. taką, która spełnia najwięcej warunków po stronie "lewej" (LHS) -4. Zastosuj wybraną regułę i wprowadź nowy element wiedzy do stanu problemu -5. Powtórz od kroku 1. + - Wybierz *bardziej specyficzną* regułę, czyli tę, która spełnia najwięcej warunków po lewej stronie (LHS) +4. Zastosuj wybraną regułę i wstaw nowy element wiedzy do stanu problemu +5. Powtarzaj od kroku 1. -Jednak w niektórych przypadkach możemy chcieć zacząć od pustej wiedzy o problemie i zadawać pytania, które pomogą nam dojść do wniosku. Na przykład podczas diagnozowania medycznego zazwyczaj nie wykonujemy wszystkich analiz medycznych z góry przed rozpoczęciem diagnozowania pacjenta. Raczej chcemy przeprowadzać analizy, gdy trzeba podjąć decyzję. +W niektórych przypadkach możemy chcieć zacząć z pustą wiedzą o problemie i zadawać pytania, które pomogą nam dojść do wniosku. Na przykład podczas diagnozy medycznej zwykle nie wykonujemy wszystkich badań przed rozpoczęciem diagnozy pacjenta. Raczej chcemy wykonać badania, gdy decyzja musi zostać podjęta. -Ten proces można modelować za pomocą **wnioskowania wstecznego**. Jest on napędzany przez **cel** - wartość atrybutu, którą chcemy znaleźć: +Proces ten można modelować za pomocą **wnioskowania w tył**. Jest on napędzany przez **cel** – wartość atrybutu, której szukamy: -1. Wybierz wszystkie reguły, które mogą dać nam wartość celu (tj. z celem po stronie "prawej" (RHS)) - zestaw konfliktów -1. Jeśli nie ma reguł dla tego atrybutu lub istnieje reguła mówiąca, że powinniśmy zapytać użytkownika o wartość - zapytaj o nią, w przeciwnym razie: -1. Użyj strategii rozwiązywania konfliktów, aby wybrać jedną regułę, którą będziemy używać jako *hipotezę* - spróbujemy ją udowodnić -1. Rekurencyjnie powtórz proces dla wszystkich atrybutów w LHS reguły, próbując je udowodnić jako cele -1. Jeśli w dowolnym momencie proces się nie powiedzie - użyj innej reguły w kroku 3. +1. Wybierz wszystkie reguły, które mogą nam dać wartość celu (czyli z celem po prawej stronie ("right-hand-side")) – zbiór konfliktu +1. Jeśli nie ma reguł dla tego atrybutu albo jest reguła mówiąca, że powinniśmy zapytać użytkownika o tę wartość – zapytaj, w przeciwnym razie: +1. Użyj strategii rozstrzygania konfliktów, aby wybrać jedną regułę, której użyjemy jako *hipotezy* – spróbujemy ją udowodnić +1. Rekurencyjnie powtarzaj proces dla wszystkich atrybutów po lewej stronie reguły, próbując udowodnić je jako cele +1. Jeśli w dowolnym momencie proces zawiedzie – użyj innej reguły w kroku 3. -> ✅ W jakich sytuacjach wnioskowanie w przód jest bardziej odpowiednie? A wnioskowanie wstecz? +> ✅ W jakich sytuacjach wnioskowanie w przód jest bardziej odpowiednie? A kiedy wnioskowanie w tył? -### Implementacja systemów ekspertowych +### Implementacja systemów eksperckich -Systemy ekspertowe można implementować za pomocą różnych narzędzi: +Systemy eksperckie można implementować przy użyciu różnych narzędzi: -* Programowanie ich bezpośrednio w jakimś języku programowania wysokiego poziomu. Nie jest to najlepszy pomysł, ponieważ główną zaletą systemu opartego na wiedzy jest to, że wiedza jest oddzielona od wnioskowania, a potencjalnie ekspert dziedzinowy powinien być w stanie pisać reguły bez rozumienia szczegółów procesu wnioskowania. -* Korzystanie z **powłoki systemów ekspertowych**, tj. systemu specjalnie zaprojektowanego do wypełniania wiedzą za pomocą jakiegoś języka reprezentacji wiedzy. +* Programowanie ich bezpośrednio w wysokopoziomowym języku programowania. Nie jest to najlepszy pomysł, ponieważ główną zaletą systemu opartego na wiedzy jest oddzielenie wiedzy od wnioskowania, i potencjalnie ekspert z danej dziedziny powinien móc pisać reguły bez rozumienia szczegółów procesu wnioskowania. +* Używanie **powłoki systemu eksperckiego**, czyli systemu specjalnie zaprojektowanego do wypełniania wiedzą przy użyciu jakiegoś języka reprezentacji wiedzy. -## ✍️ Ćwiczenie: Wnioskowanie o zwierzętach +## ✍️ Ćwiczenie: wnioskowanie o zwierzętach -Zobacz [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) jako przykład implementacji systemu ekspertowego z wnioskowaniem w przód i wstecz. +Zobacz [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) jako przykład implementacji systemu ekspertowego wnioskowania w przód i w tył. -> **Note**: Ten przykład jest dość prosty i tylko daje wyobrażenie, jak wygląda system ekspertowy. Gdy zaczniesz tworzyć taki system, zauważysz pewne *inteligentne* zachowanie dopiero po osiągnięciu pewnej liczby reguł, około 200+. W pewnym momencie reguły stają się zbyt skomplikowane, aby wszystkie je zapamiętać, i wtedy możesz zacząć się zastanawiać, dlaczego system podejmuje określone decyzje. Jednak ważną cechą systemów opartych na wiedzy jest to, że zawsze można *wyjaśnić*, jak podjęto każdą decyzję. +> **Uwaga**: Ten przykład jest dość prosty i jedynie pokazuje, jak wygląda system ekspercki. Gdy zaczniesz tworzyć taki system, zauważysz *inteligentne* zachowanie dopiero po osiągnięciu pewnej liczby reguł, około 200+. W pewnym momencie reguły stają się zbyt skomplikowane, by pamiętać je wszystkie, i wtedy możesz zacząć się zastanawiać, dlaczego system podejmuje określone decyzje. Jednak ważną cechą systemów opartych na wiedzy jest to, że zawsze możesz *wyjaśnić* dokładnie, jak podjęto jakąkolwiek decyzję. -## Ontologie i sieć semantyczna +## Ontologie i Semantyczny Web -Pod koniec XX wieku pojawiła się inicjatywa wykorzystania reprezentacji wiedzy do oznaczania zasobów internetowych, aby możliwe było znajdowanie zasobów odpowiadających bardzo specyficznym zapytaniom. Ten ruch nazwano **siecią semantyczną**, a opierał się na kilku koncepcjach: +Pod koniec XX wieku zainicjowano inicjatywę wykorzystania reprezentacji wiedzy do oznaczania zasobów internetowych, tak aby można było łatwo znaleźć zasoby odpowiadające bardzo specyficznym zapytaniom. Ruch ten nazwano **Semantycznym Webem** i opierał się na kilku koncepcjach: -- Specjalna reprezentacja wiedzy oparta na **[logikach deskryptywnych](https://en.wikipedia.org/wiki/Description_logic)** (DL). Jest podobna do reprezentacji wiedzy ramowej, ponieważ buduje hierarchię obiektów z właściwościami, ale ma formalną semantykę logiczną i wnioskowanie. Istnieje cała rodzina DL, które balansują między ekspresywnością a algorytmiczną złożonością wnioskowania. -- Rozproszona reprezentacja wiedzy, gdzie wszystkie pojęcia są reprezentowane przez globalny identyfikator URI, co umożliwia tworzenie hierarchii wiedzy obejmujących internet. +- Specjalna reprezentacja wiedzy bazująca na **[logikach opisowych](https://en.wikipedia.org/wiki/Description_logic)** (DL). Jest podobna do reprezentacji ramowej, ponieważ buduje hierarchię obiektów z właściwościami, ale ma formalną semantykę logiczną i wnioskowanie. Istnieje cała rodzina DL, które wyważają ekspresywność i algorytmiczną złożoność wnioskowania. +- Rozproszona reprezentacja wiedzy, gdzie wszystkie pojęcia są reprezentowane przez globalne identyfikatory URI, co umożliwia tworzenie hierarchii wiedzy obejmującej cały internet. - Rodzina języków opartych na XML do opisu wiedzy: RDF (Resource Description Framework), RDFS (RDF Schema), OWL (Ontology Web Language). -Podstawowym pojęciem w Semantic Web jest **ontologia**. Odnosi się ona do jawnej specyfikacji dziedziny problemowej przy użyciu formalnej reprezentacji wiedzy. Najprostsza ontologia może być po prostu hierarchią obiektów w danej dziedzinie, ale bardziej złożone ontologie zawierają reguły, które mogą być używane do wnioskowania. +Kluczowym pojęciem w Semantic Web jest pojęcie **Ontologii**. Odnosi się ono do jednoznacznej specyfikacji dziedziny problemowej z użyciem formalnej reprezentacji wiedzy. Najprostsza ontologia może być tylko hierarchią obiektów w dziedzinie problemu, ale bardziej złożone ontologie będą zawierać reguły, które mogą być używane do wnioskowania. -W Semantic Web wszystkie reprezentacje opierają się na trójkach. Każdy obiekt i każda relacja są jednoznacznie identyfikowane przez URI. Na przykład, jeśli chcemy stwierdzić fakt, że ten program AI Curriculum został opracowany przez Dmitry Soshnikova 1 stycznia 2022 roku, oto trójki, których możemy użyć: +W semantic web wszystkie reprezentacje oparte są na trójkach. Każdy obiekt i każda relacja są jednoznacznie identyfikowane przez URI. Na przykład, jeśli chcemy stwierdzić fakt, że ten AI Curriculum został opracowany przez Dmitry'ego Soshnikova 1 stycznia 2022 - oto trójki, które możemy użyć: - + ``` -http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 13, 2007” +http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 1, 2022” http://github.com/microsoft/ai-for-beginners http://purl.org/dc/elements/1.1/creator http://soshnikov.com ``` -> ✅ Tutaj `http://www.example.com/terms/creation-date` i `http://purl.org/dc/elements/1.1/creator` to dobrze znane i powszechnie akceptowane URI do wyrażania pojęć *twórca* i *data utworzenia*. +> ✅ Tutaj `http://www.example.com/terms/creation-date` i `http://purl.org/dc/elements/1.1/creator` to dobrze znane i powszechnie akceptowane URI do wyrażenia pojęć *twórca* i *data utworzenia*. -W bardziej złożonym przypadku, jeśli chcemy zdefiniować listę twórców, możemy użyć struktur danych zdefiniowanych w RDF. +W bardziej skomplikowanym przypadku, jeśli chcemy zdefiniować listę twórców, możemy użyć struktur danych zdefiniowanych w RDF. - + > Diagramy powyżej autorstwa [Dmitry Soshnikov](http://soshnikov.com) -Postęp w budowaniu Semantic Web został częściowo spowolniony przez sukces wyszukiwarek i technik przetwarzania języka naturalnego, które pozwalają na wydobywanie danych strukturalnych z tekstu. Jednak w niektórych obszarach wciąż podejmowane są znaczące wysiłki w celu utrzymania ontologii i baz wiedzy. Kilka projektów wartych uwagi: +Postęp w budowie Semantic Web został częściowo spowolniony przez sukces wyszukiwarek i technik przetwarzania języka naturalnego, które pozwalają wydobywać ustrukturyzowane dane z tekstu. Jednak w niektórych obszarach nadal podejmuje się znaczne wysiłki w utrzymaniu ontologii i baz wiedzy. Kilka projektów wartych uwagi: -* [WikiData](https://wikidata.org/) to zbiór maszynowo czytelnych baz wiedzy powiązanych z Wikipedią. Większość danych jest wydobywana z *InfoBoxów* Wikipedii, czyli fragmentów strukturalnych treści na stronach Wikipedii. Możesz [zapytania](https://query.wikidata.org/) do WikiData w SPARQL, specjalnym języku zapytań dla Semantic Web. Oto przykładowe zapytanie, które pokazuje najpopularniejsze kolory oczu wśród ludzi: +* [WikiData](https://wikidata.org/) to zbiór maszynowo odczytywalnych baz wiedzy powiązanych z Wikipedią. Większość danych pozyskiwana jest z Wikipedia *InfoBoxes*, fragmentów ustrukturyzowanej zawartości na stronach Wikipedii. Można [zapytaniać](https://query.wikidata.org/) wikidatę w SPARQL, specjalnym języku zapytań dla Semantic Web. Oto przykładowe zapytanie wyświetlające najpopularniejsze kolory oczu wśród ludzi: ```sparql #defaultView:BubbleChart @@ -206,47 +206,51 @@ WHERE GROUP BY ?eyeColorLabel ``` -* [DBpedia](https://www.dbpedia.org/) to kolejna inicjatywa podobna do WikiData. +* [DBpedia](https://www.dbpedia.org/) to kolejny podobny projekt do WikiData. -> ✅ Jeśli chcesz eksperymentować z budowaniem własnych ontologii lub otwieraniem istniejących, istnieje świetny wizualny edytor ontologii o nazwie [Protégé](https://protege.stanford.edu/). Pobierz go lub użyj online. +> ✅ Jeśli chcesz eksperymentować z tworzeniem własnych ontologii lub otwieraniem istniejących, jest świetny wizualny edytor ontologii o nazwie [Protégé](https://protege.stanford.edu/). Pobierz go lub użyj online. - + -*Edytor Web Protégé otwarty z ontologią rodziny Romanowów. Zrzut ekranu autorstwa Dmitry Soshnikov* +*Edytor Web Protégé otwarty na ontologii Rodziny Romanowów. Zrzut ekranu Dmitry Soshnikov* ## ✍️ Ćwiczenie: Ontologia Rodziny -Zobacz [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) jako przykład użycia technik Semantic Web do rozumowania o relacjach rodzinnych. Weźmiemy drzewo genealogiczne przedstawione w popularnym formacie GEDCOM oraz ontologię relacji rodzinnych i zbudujemy graf wszystkich relacji rodzinnych dla danego zestawu osób. +Zobacz [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) jako przykład wykorzystania technik Semantic Web do rozumowania o relacjach rodzinnych. Weźmiemy drzewo genealogiczne reprezentowane w popularnym formacie GEDCOM oraz ontologię relacji rodzinnych i zbudujemy graf wszystkich relacji rodzinnych dla wskazanego zestawu osób. ## Microsoft Concept Graph -W większości przypadków ontologie są starannie tworzone ręcznie. Jednak możliwe jest również **wydobywanie** ontologii z danych niestrukturalnych, na przykład z tekstów w języku naturalnym. +W większości przypadków ontologie są starannie tworzone ręcznie. Możliwe jest jednak także **wydobywanie** ontologii z danych nieustrukturyzowanych, na przykład z tekstów w języku naturalnym. -Jednym z takich prób było przedsięwzięcie Microsoft Research, które zaowocowało [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). +Jednym z takich przedsięwzięć było Microsoft Research, które zaowocowało [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). -Jest to duży zbiór jednostek grupowanych za pomocą relacji dziedziczenia `is-a`. Pozwala odpowiadać na pytania typu "Czym jest Microsoft?" - odpowiedź może brzmieć "firmą z prawdopodobieństwem 0,87 oraz marką z prawdopodobieństwem 0,75". +Jest to duży zbiór bytów pogrupowanych za pomocą relacji dziedziczenia `is-a`. Pozwala na odpowiadanie na pytania takie jak „Czym jest Microsoft?” – odpowiedzią może być coś w stylu „firma z prawdopodobieństwem 0.87 oraz marka z prawdopodobieństwem 0.75”. -Graf jest dostępny zarówno jako REST API, jak i jako duży plik tekstowy do pobrania, który zawiera wszystkie pary jednostek. +Graf jest dostępny albo jako REST API, albo jako duży plik tekstowy do pobrania, który wymienia wszystkie pary bytów. -## ✍️ Ćwiczenie: Graf Konceptów +## ✍️ Ćwiczenie: Graf Pojęć -Wypróbuj notebook [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb), aby zobaczyć, jak można użyć Microsoft Concept Graph do grupowania artykułów prasowych w kilka kategorii. +Wypróbuj notatnik [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb), aby zobaczyć, jak możemy wykorzystać Microsoft Concept Graph do grupowania artykułów informacyjnych na kilka kategorii. ## Podsumowanie -Obecnie AI często jest uważane za synonim *uczenia maszynowego* lub *sieci neuronowych*. Jednak człowiek wykazuje również zdolność do jawnego rozumowania, co jest czymś, czego obecnie nie obsługują sieci neuronowe. W rzeczywistych projektach jawne rozumowanie wciąż jest używane do wykonywania zadań wymagających wyjaśnień lub możliwości kontrolowanej modyfikacji zachowania systemu. +W dzisiejszych czasach AI często jest uważana za synonim *uczenia maszynowego* lub *sieci neuronowych*. Jednak człowiek wykazuje także jawne rozumowanie, czego obecnie sieci neuronowe nie potrafią obsłużyć. W projektach rzeczywistych jawne rozumowanie jest nadal używane do wykonywania zadań wymagających wyjaśnień lub do możliwość kontrolowanego modyfikowania zachowania systemu. ## 🚀 Wyzwanie -W notebooku Ontologia Rodziny związanym z tą lekcją istnieje możliwość eksperymentowania z innymi relacjami rodzinnymi. Spróbuj odkryć nowe powiązania między osobami w drzewie genealogicznym. +W notatniku Ontologii Rodziny powiązanym z tą lekcją jest możliwość eksperymentowania z innymi relacjami rodzinnymi. Spróbuj odkryć nowe powiązania między osobami w drzewie genealogicznym. ## [Quiz po wykładzie](https://ff-quizzes.netlify.app/en/ai/quiz/4) -## Przegląd i Samodzielna Nauka +## Przegląd i samodzielna nauka -Przeprowadź badania w internecie, aby odkryć obszary, w których ludzie próbowali kwantyfikować i kodować wiedzę. Przyjrzyj się taksonomii Blooma i cofnij się w historii, aby dowiedzieć się, jak ludzie próbowali zrozumieć swój świat. Zbadaj pracę Linneusza nad stworzeniem taksonomii organizmów i zobacz, jak Dmitrij Mendelejew stworzył sposób opisu i grupowania pierwiastków chemicznych. Jakie inne interesujące przykłady możesz znaleźć? +Zrób trochę badań w internecie, aby odkryć obszary, w których ludzie próbowali kwantyfikować i kodyfikować wiedzę. Spójrz na taksonomię Blooma oraz cofnij się w historii, aby poznać, jak ludzie próbowali zrozumieć świat. Zbadaj pracę Linneusza tworzącego taksonomię organizmów i obserwuj, jak Dmitrij Mendelejew stworzył system opisu i grupowania pierwiastków chemicznych. Jakie inne ciekawe przykłady możesz znaleźć? **Zadanie**: [Zbuduj Ontologię](assignment.md) --- + +**Zastrzeżenie**: +Niniejszy dokument został przetłumaczony przy użyciu usługi tłumaczenia AI [Co-op Translator](https://github.com/Azure/co-op-translator). Chociaż dążymy do dokładności, prosimy pamiętać, że automatyczne tłumaczenia mogą zawierać błędy lub niedokładności. Oryginalny dokument w języku źródłowym należy traktować jako źródło wiążące. W przypadku informacji krytycznych zaleca się skorzystanie z profesjonalnego tłumaczenia wykonanego przez człowieka. Nie ponosimy odpowiedzialności za jakiekolwiek nieporozumienia lub błędne interpretacje wynikające z korzystania z tego tłumaczenia. + \ No newline at end of file diff --git a/translations/pt/README.md b/translations/pt/README.md index cc8ed108..6d83275b 100644 --- a/translations/pt/README.md +++ b/translations/pt/README.md @@ -1,8 +1,8 @@ -[Árabe](../ar/README.md) | [Bengali](../bn/README.md) | [Búlgaro](../bg/README.md) | [Birmanês (Myanmar)](../my/README.md) | [Chinês (Simplificado)](../zh/README.md) | [Chinês (Tradicional, Hong Kong)](../hk/README.md) | [Chinês (Tradicional, Macau)](../mo/README.md) | [Chinês (Tradicional, Taiwan)](../tw/README.md) | [Croata](../hr/README.md) | [Checo](../cs/README.md) | [Dinamarquês](../da/README.md) | [Holandês](../nl/README.md) | [Estónio](../et/README.md) | [Finlandês](../fi/README.md) | [Francês](../fr/README.md) | [Alemão](../de/README.md) | [Grego](../el/README.md) | [Hebraico](../he/README.md) | [Hindi](../hi/README.md) | [Húngaro](../hu/README.md) | [Indonésio](../id/README.md) | [Italiano](../it/README.md) | [Japonês](../ja/README.md) | [Kannada](../kn/README.md) | [Coreano](../ko/README.md) | [Lituano](../lt/README.md) | [Malaio](../ms/README.md) | [Malaiala](../ml/README.md) | [Marata](../mr/README.md) | [Nepali](../ne/README.md) | [Pidgin Nigeriano](../pcm/README.md) | [Norueguês](../no/README.md) | [Persa (Farsi)](../fa/README.md) | [Polaco](../pl/README.md) | [Português (Brasil)](../br/README.md) | [Português (Portugal)](./README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romeno](../ro/README.md) | [Russo](../ru/README.md) | [Sérvio (Cirílico)](../sr/README.md) | [Eslovaco](../sk/README.md) | [Esloveno](../sl/README.md) | [Espanhol](../es/README.md) | [Suaíli](../sw/README.md) | [Sueco](../sv/README.md) | [Tagalo (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Tailandês](../th/README.md) | [Turco](../tr/README.md) | [Ucraniano](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamita](../vi/README.md) +[Árabe](../ar/README.md) | [Bengali](../bn/README.md) | [Búlgaro](../bg/README.md) | [Birmanês (Myanmar)](../my/README.md) | [Chinês (Simplificado)](../zh/README.md) | [Chinês (Tradicional, Hong Kong)](../hk/README.md) | [Chinês (Tradicional, Macau)](../mo/README.md) | [Chinês (Tradicional, Taiwan)](../tw/README.md) | [Croata](../hr/README.md) | [Checo](../cs/README.md) | [Dinamarquês](../da/README.md) | [Holandês](../nl/README.md) | [Estónio](../et/README.md) | [Finlandês](../fi/README.md) | [Francês](../fr/README.md) | [Alemão](../de/README.md) | [Grego](../el/README.md) | [Hebraico](../he/README.md) | [Hindi](../hi/README.md) | [Húngaro](../hu/README.md) | [Indonésio](../id/README.md) | [Italiano](../it/README.md) | [Japonês](../ja/README.md) | [Kannada](../kn/README.md) | [Coreano](../ko/README.md) | [Lituano](../lt/README.md) | [Malaio](../ms/README.md) | [Malaiala](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Pidgin Nigeriano](../pcm/README.md) | [Norueguês](../no/README.md) | [Persa (Farsi)](../fa/README.md) | [Polaco](../pl/README.md) | [Português (Brasil)](../br/README.md) | [Português (Portugal)](./README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romeno](../ro/README.md) | [Russo](../ru/README.md) | [Sérvio (Cirílico)](../sr/README.md) | [Eslovaco](../sk/README.md) | [Esloveno](../sl/README.md) | [Espanhol](../es/README.md) | [Suaíli](../sw/README.md) | [Sueco](../sv/README.md) | [Tagalo (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Tailandês](../th/README.md) | [Turco](../tr/README.md) | [Ucraniano](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamita](../vi/README.md) > **Prefere Clonar Localmente?** -> Este repositório inclui 50+ traduções de idiomas que aumentam significativamente o tamanho do download. Para clonar sem traduções, use sparse checkout: +> Este repositório inclui mais de 50 traduções para diferentes idiomas, o que aumenta significativamente o tamanho do download. Para clonar sem traduções, use o sparse checkout: > ```bash > git clone --filter=blob:none --sparse https://github.com/microsoft/AI-For-Beginners.git > cd AI-For-Beginners > git sparse-checkout set --no-cone '/*' '!translations' '!translated_images' > ``` -> Isto fornece tudo o que precisa para completar o curso com um download muito mais rápido. +> Isto dá-lhe tudo o que precisa para completar o curso com um download muito mais rápido. -**Se desejar suportar línguas de tradução adicionais, estão listadas [aqui](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** +**Se desejar que idiomas adicionais sejam suportados, estão listados [aqui](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** ## Junte-se à Comunidade [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -## O que irá aprender +## O que vai aprender -**[Mapa Mental do Curso](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** +**[Mapa mental do Curso](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** -Neste currículo, irá aprender: +Neste currículo, aprenderá: -* Diferentes abordagens à Inteligência Artificial, incluindo a abordagem "antiga" simbólica com **Representação do Conhecimento** e raciocínio ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). -* **Redes Neurais** e **Aprendizagem Profunda**, que estão no centro da IA moderna. Iremos ilustrar os conceitos por trás destes tópicos importantes usando código em dois dos frameworks mais populares - [TensorFlow](http://Tensorflow.org) e [PyTorch](http://pytorch.org). -* **Arquiteturas Neurais** para trabalhar com imagens e texto. Cobriremos modelos recentes, embora possam não estar totalmente atualizados com o estado da arte. -* Abordagens de IA menos populares, como **Algoritmos Genéticos** e **Sistemas Multi-Agentes**. +* Diferentes abordagens da Inteligência Artificial, incluindo a "velha boa" abordagem simbólica com **Representação do Conhecimento** e raciocínio ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). +* **Redes Neurais** e **Aprendizagem Profunda**, que estão no centro da IA moderna. Ilustraremos os conceitos por trás destes tópicos importantes usando código em dois dos frameworks mais populares - [TensorFlow](http://Tensorflow.org) e [PyTorch](http://pytorch.org). +* **Arquiteturas Neurais** para trabalhar com imagens e texto. Cobriremos modelos recentes, mas poderão faltar um pouco no estado da arte. +* Abordagens menos populares de IA, como **Algoritmos Genéticos** e **Sistemas Multi-Agente**. O que não será coberto neste currículo: > [Encontre todos os recursos adicionais deste curso na nossa coleção Microsoft Learn](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* Casos de uso empresarial de **IA nos Negócios**. Considere fazer o caminho de aprendizagem [Introdução à IA para utilizadores de negócios](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) na Microsoft Learn, ou a [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), desenvolvida em cooperação com a [INSEAD](https://www.insead.edu/). +* Casos de negócio do uso de **IA nos Negócios**. Considere seguir o percurso de aprendizagem [Introdução à IA para utilizadores empresariais](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) na Microsoft Learn, ou a [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), desenvolvida em cooperação com a [INSEAD](https://www.insead.edu/). * **Aprendizagem Automática Clássica**, que está bem descrita no nosso [Currículo de Aprendizagem Automática para Iniciantes](http://github.com/Microsoft/ML-for-Beginners). -* Aplicações práticas de IA construídas com **[Serviços Cognitivos](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. Para isto, recomendamos que comece pelos módulos Microsoft Learn para [visão](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [processamento de linguagem natural](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[IA Generativa com Azure OpenAI Service](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** e outros. -* **Frameworks Específicos de Aprendizagem Automática na Nuvem**, como o [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum), [Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum), ou [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Considere utilizar os caminhos de aprendizagem [Construir e operar soluções de machine learning com Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) e [Construir e operar soluções de Aprendizagem Automática com Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum). -* **IA Conversacional** e **Chat Bots**. Há um caminho de aprendizagem separado [Criar soluções de IA conversacional](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum), poderá também consultar [este post no blog](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) para mais detalhes. -* **Matemática Avançada** por trás do deep learning. Para isso, recomendamos [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) de Ian Goodfellow, Yoshua Bengio e Aaron Courville, que também está disponível online em [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). +* Aplicações práticas de IA construídas usando **[Serviços Cognitivos](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. Para isso, recomendamos que comece com módulos Microsoft Learn para [visão](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [processamento de linguagem natural](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[IA Generativa com Azure OpenAI Service](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** e outros. +* **Frameworks Cloud específicas para ML**, como [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum), [Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum), ou [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Considere usar os percursos de aprendizagem [Construir e operar soluções de aprendizagem automática com Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) e [Construir e Operar Soluções de Aprendizagem Automática com Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum). +* **IA Conversacional** e **Chat Bots**. Existe um percurso de aprendizagem separado [Criar soluções de IA conversacional](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum), e pode também consultar [este post de blog](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) para mais detalhes. +* **Matemática Profunda** por trás do deep learning. Para isso, recomendamos [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) por Ian Goodfellow, Yoshua Bengio e Aaron Courville, que está também disponível online em [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). -Para uma introdução suave a tópicos de _IA na Nuvem_, pode considerar seguir o caminho de aprendizagem [Começar com inteligência artificial no Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum). +Para uma introdução suave aos temas de _IA na Cloud_ pode considerar seguir o percurso de aprendizagem [Começar com inteligência artificial no Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum). # Conteúdo -| | Link da Lição | PyTorch/Keras/TensorFlow | Laboratório | +| | Link da Aula | PyTorch/Keras/TensorFlow | Laboratório | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | -| 0 | [Configuração do Curso](./lessons/0-course-setup/setup.md) | [Configure o Seu Ambiente de Desenvolvimento](./lessons/0-course-setup/how-to-run.md) | | +| 0 | [Configuração do Curso](./lessons/0-course-setup/setup.md) | [Configurar o Seu Ambiente de Desenvolvimento](./lessons/0-course-setup/how-to-run.md) | | | I | [**Introdução à IA**](./lessons/1-Intro/README.md) | | | | 01 | [Introdução e História da IA](./lessons/1-Intro/README.md) | - | - | | II | **IA Simbólica** | | 02 | [Representação do Conhecimento e Sistemas Especialistas](./lessons/2-Symbolic/README.md) | [Sistemas Especialistas](./lessons/2-Symbolic/Animals.ipynb) / [Ontologia](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Grafo de Conceitos](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | | III | [**Introdução às Redes Neurais**](./lessons/3-NeuralNetworks/README.md) ||| -| 03 | [Perceptron](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [Caderno](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [Laboratório](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | -| 04 | [Perceptron Multi-Camada e Criação do nosso Próprio Framework](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Caderno](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Laboratório](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | -| 05 | [Introdução a Frameworks (PyTorch/TensorFlow) e Sobreajuste](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [PyTorch](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [Keras](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Laboratório](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | +| 03 | [Perceptron](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [Notebook](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [Lab](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | +| 04 | [Perceptron Multicamadas e Criação do nosso próprio Framework](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Notebook](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Lab](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | +| 05 | [Introdução a Frameworks (PyTorch/TensorFlow) e Overfitting](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [PyTorch](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [Keras](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | | IV | [**Visão Computacional**](./lessons/4-ComputerVision/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [Explore Visão Computacional na Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | -| 06 | [Introdução à Visão Computacional. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Caderno](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Laboratório](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | -| 07 | [Redes Neuronais Convolucionais](./lessons/4-ComputerVision/07-ConvNets/README.md) & [Arquiteturas CNN](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [Laboratório](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | -| 08 | [Redes Pré-treinadas e Transferência de Aprendizagem](./lessons/4-ComputerVision/08-TransferLearning/README.md) e [Truques de Treino](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Laboratório](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | +| 06 | [Introdução à Visão Computacional. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Notebook](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Lab](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | +| 07 | [Redes Neuronais Convolucionais](./lessons/4-ComputerVision/07-ConvNets/README.md) & [Arquiteturas CNN](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [Lab](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | +| 08 | [Redes Pré-treinadas e Transfer Learning](./lessons/4-ComputerVision/08-TransferLearning/README.md) and [Dicas de Treino](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | | 09 | [Autoencoders e VAEs](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | -| 10 | [Redes Gerativas Adversárias e Transferência de Estilo Artístico](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | -| 11 | [Deteção de Objetos](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [Laboratório](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | +| 10 | [Redes Generativas Adversárias & Transferência de Estilo Artístico](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | +| 11 | [Detecção de Objetos](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [Lab](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | | 12 | [Segmentação Semântica. U-Net](./lessons/4-ComputerVision/12-Segmentation/README.md) | [PyTorch](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb) | | | V | [**Processamento de Linguagem Natural**](./lessons/5-NLP/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [Explore Processamento de Linguagem Natural na Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| | 13 | [Representação de Texto. Bow/TF-IDF](./lessons/5-NLP/13-TextRep/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb) | | -| 14 | [Embedding Semânticos de Palavras. Word2Vec e GloVe](./lessons/5-NLP/14-Embeddings/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb) | | -| 15 | [Modelagem de Linguagem. Treinando os seus próprios embeddings](./lessons/5-NLP/15-LanguageModeling/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [Laboratório](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | +| 14 | [Embeddings Semânticos de Palavras. Word2Vec e GloVe](./lessons/5-NLP/14-Embeddings/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb) | | +| 15 | [Modelação de Linguagem. Treinar os seus próprios embeddings](./lessons/5-NLP/15-LanguageModeling/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [Lab](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | | 16 | [Redes Neuronais Recorrentes](./lessons/5-NLP/16-RNN/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNTF.ipynb) | | -| 17 | [Redes Recorrentes Gerativas](./lessons/5-NLP/17-GenerativeNetworks/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb) | [Laboratório](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | +| 17 | [Redes Recorrentes Generativas](./lessons/5-NLP/17-GenerativeNetworks/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb) | [Lab](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | | 18 | [Transformers. BERT.](./lessons/5-NLP/18-Transformers/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb) /[TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersTF.ipynb) | | -| 19 | [Reconhecimento de Entidades Nomeadas](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [Laboratório](./lessons/5-NLP/19-NER/lab/README.md) | -| 20 | [Modelos de Linguagem em Grande Escala, Programação de Prompts e Tarefas Few-Shot](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | +| 19 | [Reconhecimento de Entidades Nomeadas](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [Lab](./lessons/5-NLP/19-NER/lab/README.md) | +| 20 | [Modelos de Linguagem Grande, Programação de Prompt e Tarefas Few-Shot](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | | VI | **Outras Técnicas de IA** || | -| 21 | [Algoritmos Genéticos](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Caderno](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | -| 22 | [Aprendizagem por Reforço Profunda](./lessons/6-Other/22-DeepRL/README.md) | [PyTorch](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[TensorFlow](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [Laboratório](./lessons/6-Other/22-DeepRL/lab/README.md) | +| 21 | [Algoritmos Genéticos](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Notebook](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | +| 22 | [Aprendizagem por Reforço Profunda](./lessons/6-Other/22-DeepRL/README.md) | [PyTorch](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[TensorFlow](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [Lab](./lessons/6-Other/22-DeepRL/lab/README.md) | | 23 | [Sistemas Multi-Agente](./lessons/6-Other/23-MultiagentSystems/README.md) | | | | VII | **Ética na IA** | | | | 24 | [Ética na IA e IA Responsável](./lessons/7-Ethics/README.md) | [Microsoft Learn: Princípios de IA Responsável](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | | IX | **Extras** | | | -| 25 | [Redes Multi-Modais, CLIP e VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Caderno](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | +| 25 | [Redes Multimodais, CLIP e VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Notebook](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | -## Cada aula contém +## Cada lição contém -* Material de pré-leitura -* Cadernos Jupyter executáveis, frequentemente específicos do framework (**PyTorch** ou **TensorFlow**). O caderno executável contém também muito material teórico, pelo que para perceber o tópico deve explorar pelo menos uma versão do caderno (PyTorch ou TensorFlow). -* **Laboratórios** disponíveis para alguns temas, que oferecem uma oportunidade para aplicar o material aprendido a um problema específico. -* Algumas secções contêm links para módulos [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) que abordam tópicos relacionados. +* Material para pré-leitura +* Jupyter Notebooks executáveis, que são frequentemente específicos para o framework (**PyTorch** ou **TensorFlow**). O notebook executável também contém muito material teórico, por isso para entender o tópico é necessário percorrer pelo menos uma versão do notebook (ou PyTorch ou TensorFlow). +* **Laboratórios** disponíveis para alguns tópicos, que dão a oportunidade de colocar em prática o material aprendido num problema específico. +* Algumas secções contêm ligações para módulos do [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) que cobrem tópicos relacionados. ## Começar -### 🎯 Novo em IA? Comece aqui! +### 🎯 Novo em IA? Comece Aqui! -Se é completamente novo em IA e quer exemplos rápidos e práticos, confira os nossos [**Exemplos para Iniciantes**](./examples/README.md)! Estes incluem: +Se é completamente novo em IA e quer exemplos rápidos e práticos, consulte os nossos [**Exemplos para Iniciantes**](./examples/README.md)! Estes incluem: - 🌟 **Olá Mundo IA** - O seu primeiro programa de IA (reconhecimento de padrões) -- 🧠 **Rede Neural Simples** - Construa uma rede neural do zero -- 🖼️ **Classificador de Imagens** - Classifique imagens com comentários detalhados -- 💬 **Sentimento do Texto** - Analise o texto positivo/negativo +- 🧠 **Rede Neural Simples** - Construir uma rede neural do zero +- 🖼️ **Classificador de Imagens** - Classificar imagens com comentários detalhados +- 💬 **Análise de Sentimento do Texto** - Analisar texto positivo/negativo -Estes exemplos foram concebidos para o ajudar a compreender os conceitos de IA antes de mergulhar no currículo completo. +Estes exemplos foram concebidos para ajudá-lo a compreender conceitos de IA antes de entrar no currículo completo. ### 📚 Configuração do Currículo Completo -- Criámos uma [lição de configuração](./lessons/0-course-setup/setup.md) para o ajudar a configurar o seu ambiente de desenvolvimento. - Para Educadores, criámos uma [lição de configuração do currículo](./lessons/0-course-setup/for-teachers.md) para si também! -- Como [Executar o código no VSCode ou no Codepace](./lessons/0-course-setup/how-to-run.md) +- Criámos uma [lição de configuração](./lessons/0-course-setup/setup.md) para ajudá-lo a configurar o seu ambiente de desenvolvimento. +- Para Educadores, também criámos uma [lição de configuração do currículo](./lessons/0-course-setup/for-teachers.md)! +- Como [Executar o código no VSCode ou num Codespace](./lessons/0-course-setup/how-to-run.md) Siga estes passos: @@ -146,33 +148,33 @@ Fork do Repositório: Clique no botão "Fork" no canto superior direito desta p Clone o Repositório: `git clone https://github.com/microsoft/AI-For-Beginners.git` -Não se esqueça de assinalar com uma estrela (🌟) este repositório para o encontrar mais facilmente depois. +Não se esqueça de colocar uma estrela (🌟) neste repositório para o encontrar mais facilmente depois. -## Conheça outros aprendizes +## Conheça Outros Aprendizes -Junte-se ao nosso [servidor oficial de Discord de IA](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) para conhecer e interagir com outros aprendizes que estão a fazer este curso e obter apoio. +Junte-se ao nosso [servidor oficial de Discord de IA](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) para conhecer e estabelecer contactos com outros alunos que estão a realizar este curso e obter apoio. -Se tiver feedback sobre o produto ou perguntas durante a construção, visite o nosso [Fórum de Desenvolvedores Azure AI Foundry](https://aka.ms/foundry/forum) +Se tiver feedback sobre o produto ou dúvidas enquanto constrói, visite o nosso [Fórum de Desenvolvedores Azure AI Foundry](https://aka.ms/foundry/forum) -## Questionários +## Questionários -> **Uma nota sobre questionários**: Todos os questionários estão contidos na pasta Quiz-app em etc\quiz-app, ou [Online Aqui](https://ff-quizzes.netlify.app/) Estão ligados a partir das lições e a aplicação de questionários pode ser executada localmente ou implantada no Azure; siga as instruções na pasta `quiz-app`. Estão a ser gradualmente localizados. +> **Uma nota sobre os questionários**: Todos os questionários estão contidos na pasta Quiz-app em etc\quiz-app, ou [Online Aqui](https://ff-quizzes.netlify.app/). Estão ligados dentro das lições; a aplicação de questionários pode ser executada localmente ou implantada no Azure; siga as instruções na pasta `quiz-app`. Estão a ser gradualmente localizados. -## Ajuda Necessária +## Procuramos Ajuda -Tem sugestões ou encontrou erros ortográficos ou de código? Levante uma issue ou crie um pull request. +Tem sugestões ou encontrou erros ortográficos ou de código? Abra um issue ou crie um pull request. ## Agradecimentos Especiais * **✍️ Autor Principal:** [Dmitry Soshnikov](http://soshnikov.com), PhD * **🔥 Editor:** [Jen Looper](https://twitter.com/jenlooper), PhD -* **🎨 Ilustrador Sketchnote:** [Tomomi Imura](https://twitter.com/girlie_mac) -* **✅ Criadora do Questionário:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) +* **🎨 Ilustradora dos Sketchnotes:** [Tomomi Imura](https://twitter.com/girlie_mac) +* **✅ Criadora dos Questionários:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) * **🙏 Contribuidores Principais:** [Evgenii Pishchik](https://github.com/Pe4enIks) ## Outros Currículos -A nossa equipa produz outros currículos! Confira: +A nossa equipa produz outros currículos! Veja: ### LangChain @@ -181,7 +183,7 @@ A nossa equipa produz outros currículos! Confira: --- -### Azure / Edge / MCP / Agentes +### Azure / Edge / MCP / Agents [![AZD for Beginners](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst) [![Edge AI for Beginners](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst) [![MCP for Beginners](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst) @@ -197,7 +199,7 @@ A nossa equipa produz outros currículos! Confira: --- -### Aprendizagem Básica +### Aprendizagem Fundamental [![ML for Beginners](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) [![Data Science for Beginners](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst) [![AI for Beginners](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst) @@ -216,11 +218,11 @@ A nossa equipa produz outros currículos! Confira: ## Obter Ajuda -Se ficar bloqueado ou tiver perguntas sobre como construir aplicações de IA. Junte-se a outros aprendizes e desenvolvedores experientes em discussões sobre MCP. É uma comunidade solidária onde as perguntas são bem-vindas e o conhecimento é compartilhado livremente. +Se ficar bloqueado ou tiver quaisquer dúvidas sobre a construção de aplicações de IA, junte-se a outros aprendizes e desenvolvedores experientes em discussões sobre MCP. É uma comunidade de apoio onde as perguntas são bem-vindas e o conhecimento é partilhado livremente. [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -Se tiver feedback sobre o produto ou erros durante a construção, visite: +Se tiver feedback sobre o produto ou erros enquanto constrói, visite: [![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) @@ -228,5 +230,5 @@ Se tiver feedback sobre o produto ou erros durante a construção, visite: **Aviso Legal**: -Este documento foi traduzido utilizando o serviço de tradução automática [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, por favor, tenha em conta que traduções automáticas podem conter erros ou imprecisões. O documento original na sua língua nativa deve ser considerado a fonte autoritativa. Para informações críticas, recomenda-se a tradução profissional feita por um humano. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações erradas decorrentes do uso desta tradução. +Este documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos por alcançar a precisão, por favor esteja ciente de que traduções automáticas podem conter erros ou imprecisões. O documento original na sua língua nativa deve ser considerado a fonte autorizada. Para informações críticas, recomenda-se a tradução profissional por um tradutor humano. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações erradas decorrentes do uso desta tradução. \ No newline at end of file diff --git a/translations/pt/lessons/0-course-setup/how-to-run.md b/translations/pt/lessons/0-course-setup/how-to-run.md index afdb4338..0f932be1 100644 --- a/translations/pt/lessons/0-course-setup/how-to-run.md +++ b/translations/pt/lessons/0-course-setup/how-to-run.md @@ -1,21 +1,21 @@ # Como Executar o Código -Este currículo contém muitos exemplos executáveis e laboratórios que você provavelmente vai querer executar. Para isso, é necessário ter a capacidade de executar código Python em Jupyter Notebooks fornecidos como parte deste currículo. Existem várias opções para executar o código: +Este currículo contém muitos exemplos executáveis e laboratórios que você vai querer executar. Para isso, precisa da capacidade de executar código Python nos Jupyter Notebooks fornecidos como parte deste currículo. Tem várias opções para executar o código: ## Executar localmente no seu computador -Para executar o código localmente no seu computador, será necessário ter alguma versão do Python instalada. Recomendo pessoalmente instalar o **[miniconda](https://conda.io/en/latest/miniconda.html)** - é uma instalação leve que suporta o gestor de pacotes `conda` para diferentes **ambientes virtuais** Python. +Para executar o código localmente no seu computador, é necessário uma instalação de Python. Uma recomendação é instalar o **[miniconda](https://conda.io/en/latest/miniconda.html)** - é uma instalação relativamente leve que suporta o gestor de pacotes `conda` para diferentes **ambientes virtuais** Python. -Depois de instalar o miniconda, será necessário clonar o repositório e criar um ambiente virtual para ser usado neste curso: +Depois de instalar o miniconda, clone o repositório e crie um ambiente virtual a ser usado para este curso: ```bash git clone http://github.com/microsoft/ai-for-beginners @@ -24,19 +24,19 @@ conda env create --name ai4beg --file .devcontainer/environment.yml conda activate ai4beg ``` -### Usar o Visual Studio Code com a Extensão Python +### Usar Visual Studio Code com a Extensão Python -Provavelmente, a melhor forma de usar o currículo é abri-lo no [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) com a [Extensão Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste). +Este currículo é melhor aproveitado quando aberto no [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) com a [Extensão Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste). -> **Nota**: Assim que clonar e abrir o diretório no VS Code, ele sugerirá automaticamente a instalação das extensões Python. Também será necessário instalar o miniconda, conforme descrito acima. +> **Nota**: Depois de clonar e abrir o diretório no VS Code, será sugerido automaticamente que instale extensões Python. Também terá de instalar o miniconda conforme descrito acima. -> **Nota**: Se o VS Code sugerir reabrir o repositório num container, deve recusar para usar a instalação local do Python. +> **Nota**: Se o VS Code lhe sugerir reabrir o repositório num contentor, deve recusar para usar a instalação local do Python. -### Usar o Jupyter no Navegador +### Usar Jupyter no Navegador -Também pode usar o ambiente Jupyter diretamente no navegador no seu próprio computador. Na verdade, tanto o Jupyter clássico quanto o Jupyter Hub oferecem um ambiente de desenvolvimento bastante conveniente com auto-completação, realce de código, etc. +Também pode usar um ambiente Jupyter a partir do navegador no seu próprio computador. Tanto o Jupyter clássico como o JupyterHub oferecem um ambiente de desenvolvimento conveniente com auto-completação, realce de código, etc. -Para iniciar o Jupyter localmente, vá até o diretório do curso e execute: +Para iniciar o Jupyter localmente, vá para o diretório do curso e execute: ```bash jupyter notebook @@ -45,32 +45,36 @@ ou ```bash jupyterhub ``` -Depois, pode navegar até qualquer um dos ficheiros `.ipynb`, abri-los e começar a trabalhar. +Pode então navegar para qualquer um dos ficheiros `.ipynb`, abrir e começar a trabalhar. -### Executar num container +### Executar em contentor -Uma alternativa à instalação do Python seria executar o código num container. Como o nosso repositório contém uma pasta especial `.devcontainer` que instrui como construir um container para este repositório, o VS Code oferecerá a opção de reabrir o código num container. Isto exigirá a instalação do Docker e será mais complexo, por isso recomendamos esta opção para utilizadores mais experientes. +Uma alternativa à instalação do Python seria executar o código num contentor. Como o nosso repositório fornece uma pasta especial `.devcontainer` que indica como construir um contentor para este repositório, o VS Code oferece a oportunidade de reabrir o código num contentor. Isto irá requerer a instalação do Docker, e também será mais complexo, pelo que recomendamos isto para utilizadores mais experientes. -## Executar na Nuvem +## Executar na Cloud -Se não quiser instalar o Python localmente e tiver acesso a alguns recursos na nuvem, uma boa alternativa seria executar o código na nuvem. Existem várias formas de fazer isso: +Se não pretende instalar Python localmente, e tem acesso a alguns recursos na cloud - uma boa alternativa seria executar o código na cloud. Existem várias formas de o fazer: -* Usar o **[GitHub Codespaces](https://github.com/features/codespaces)**, que é um ambiente virtual criado para si no GitHub, acessível através da interface do navegador do VS Code. Se tiver acesso ao Codespaces, basta clicar no botão **Code** no repositório, iniciar um codespace e começar a trabalhar rapidamente. -* Usar o **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. O [Binder](https://mybinder.org) oferece recursos computacionais gratuitos na nuvem para pessoas como você testarem algum código no GitHub. Há um botão na página inicial para abrir o repositório no Binder - isso deve levá-lo rapidamente ao site do Binder, que construirá o container subjacente e iniciará a interface web do Jupyter para si de forma transparente. +* Usar **[GitHub Codespaces](https://github.com/features/codespaces)**, que é um ambiente virtual criado para si no GitHub, acessível através de uma interface de navegador VS Code. Se tem acesso ao Codespaces, pode simplesmente clicar no botão **Code** no repositório, iniciar um codespace, e começar a correr em pouco tempo. +* Usar **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. [Binder](https://mybinder.org) oferece recursos computacionais gratuitos providenciados na cloud para pessoas como você testarem algum código no GitHub. Existe um botão na página inicial para abrir o repositório no Binder – isto deve levá-lo rapidamente ao site binder, que vai construir um contentor subjacente e iniciar uma interface web Jupyter para si de forma integrada. -> **Nota**: Para evitar uso indevido, o Binder tem acesso a alguns recursos web bloqueados. Isso pode impedir que algum código funcione, especialmente se ele buscar modelos e/ou conjuntos de dados da Internet pública. Pode ser necessário encontrar algumas alternativas. Além disso, os recursos computacionais fornecidos pelo Binder são bastante básicos, então o treino será lento, especialmente nas lições mais complexas. +> **Nota**: Para prevenir uso indevido, o Binder tem acesso a alguns recursos web bloqueados. Isto pode impedir que algum código funcione, especialmente o que descarrega modelos e/ou conjuntos de dados da Internet pública. Pode precisar de encontrar algumas soluções alternativas. Além disso, os recursos computacionais providenciados pelo Binder são bastante básicos, pelo que o treino será lento, especialmente nas lições mais avançadas e complexas. -## Executar na Nuvem com GPU +## Executar na Cloud com GPU -Algumas das lições mais avançadas deste currículo beneficiariam muito do suporte a GPU, pois, caso contrário, o treino será extremamente lento. Existem algumas opções que pode seguir, especialmente se tiver acesso à nuvem, seja através do [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) ou da sua instituição: +Algumas das lições mais avançadas deste currículo beneficiariam muito do suporte a GPU. O treino de modelos, por exemplo, pode ser extremamente lento de outra forma. Existem algumas opções que pode seguir, especialmente se tiver acesso à cloud através do [Azure para Estudantes](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) ou da sua instituição: -* Criar uma [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) e conectá-la através do Jupyter. Pode então clonar o repositório diretamente na máquina e começar a aprender. As VMs da série NC têm suporte a GPU. +* Criar [Máquina Virtual para Ciência de Dados](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) e ligar-se a ela através do Jupyter. Pode então clonar o repositório diretamente na máquina e começar a aprender. As máquinas virtuais NC-series têm suporte a GPU. -> **Nota**: Algumas subscrições, incluindo o Azure for Students, não fornecem suporte a GPU por padrão. Pode ser necessário solicitar núcleos de GPU adicionais através de um pedido de suporte técnico. +> **Nota**: Algumas subscrições, incluindo o Azure para Estudantes, não fornecem suporte a GPU por defeito. Pode precisar de pedir núcleos de GPU adicionais através de um pedido de suporte técnico. -* Criar um [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) e usar a funcionalidade de Notebooks lá. [Este vídeo](https://azure-for-academics.github.io/quickstart/azureml-papers/) mostra como clonar um repositório para o notebook do Azure ML e começar a utilizá-lo. +* Criar um [Workspace Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) e depois usar a funcionalidade Notebook aí. [Este vídeo](https://azure-for-academics.github.io/quickstart/azureml-papers/) mostra como clonar um repositório num notebook Azure ML e começar a usar. -Também pode usar o Google Colab, que oferece algum suporte gratuito a GPU, e carregar os Jupyter Notebooks lá para executá-los um por um. +Também pode usar o Google Colab, que vem com algum suporte GPU gratuito, e carregar Jupyter Notebooks lá para executá-los um a um. -**Aviso Legal**: -Este documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, é importante notar que traduções automáticas podem conter erros ou imprecisões. O documento original na sua língua nativa deve ser considerado a fonte autoritária. Para informações críticas, recomenda-se a tradução profissional realizada por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações incorretas decorrentes do uso desta tradução. \ No newline at end of file +--- + + +**Aviso Legal**: +Este documento foi traduzido utilizando o serviço de tradução automática [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos por garantir a precisão, tenha em atenção que traduções automáticas podem conter erros ou imprecisões. O documento original na sua língua nativa deve ser considerado a fonte oficial. Para informações críticas, recomenda-se a tradução profissional por um tradutor humano. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações incorretas decorrentes do uso desta tradução. + \ No newline at end of file diff --git a/translations/pt/lessons/2-Symbolic/Animals.ipynb b/translations/pt/lessons/2-Symbolic/Animals.ipynb index e49fcf1c..50f4014c 100644 --- a/translations/pt/lessons/2-Symbolic/Animals.ipynb +++ b/translations/pt/lessons/2-Symbolic/Animals.ipynb @@ -6,25 +6,25 @@ "collapsed": true }, "source": [ - "# Implementar um Sistema Especialista de Animais\n", + "# Implementação de um Sistema Especialista em Animais\n", "\n", - "Um exemplo do [Currículo de IA para Iniciantes](http://github.com/microsoft/ai-for-beginners).\n", + "Um exemplo do [Currículo AI for Beginners](http://github.com/microsoft/ai-for-beginners).\n", "\n", - "Neste exemplo, vamos implementar um sistema simples baseado em conhecimento para determinar um animal com base em algumas características físicas. O sistema pode ser representado pela seguinte árvore AND-OR (esta é uma parte da árvore completa, podemos facilmente adicionar mais regras):\n", + "Neste exemplo, vamos implementar um sistema baseado em conhecimento simples para determinar um animal com base em algumas características físicas. O sistema pode ser representado pela seguinte árvore AND-OR (esta é uma parte da árvore completa, podemos facilmente adicionar mais regras):\n", "\n", - "![](../../../../lessons/2-Symbolic/images/AND-OR-Tree.png)\n" + "![](../../../../../../translated_images/pt/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## O nosso próprio sistema especialista com inferência retroativa\n", + "## A nossa própria shell de sistemas especialistas com inferência para trás\n", "\n", - "Vamos tentar definir uma linguagem simples para representação de conhecimento baseada em regras de produção. Utilizaremos classes Python como palavras-chave para definir as regras. Essencialmente, haverá 3 tipos de classes:\n", - "* `Ask` representa uma pergunta que precisa ser feita ao utilizador. Contém o conjunto de respostas possíveis.\n", - "* `If` representa uma regra, sendo apenas uma simplificação sintática para armazenar o conteúdo da regra.\n", - "* `AND`/`OR` são classes para representar os ramos AND/OR da árvore. Elas apenas armazenam a lista de argumentos no seu interior. Para simplificar o código, toda a funcionalidade é definida na classe-mãe `Content`.\n" + "Vamos tentar definir uma linguagem simples para a representação de conhecimento baseada em regras de produção. Vamos usar classes Python como palavras-chave para definir regras. Essencialmente, existirão 3 tipos de classes:\n", + "* `Ask` representa uma pergunta que precisa ser colocada ao utilizador. Contém o conjunto de respostas possíveis.\n", + "* `If` representa uma regra, sendo apenas um açúcar sintático para armazenar o conteúdo da regra.\n", + "* `AND`/`OR` são classes para representar ramos AND/OR da árvore. Elas apenas armazenam a lista de argumentos no seu interior. Para simplificar o código, toda a funcionalidade está definida na classe pai `Content`.\n" ] }, { @@ -66,7 +66,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "No nosso sistema, a memória de trabalho conteria a lista de **factos** como **pares atributo-valor**. A base de conhecimento pode ser definida como um grande dicionário que mapeia ações (novos factos que devem ser inseridos na memória de trabalho) para condições, expressas como expressões AND-OR. Além disso, alguns factos podem ser `Perguntados`.\n" + "No nosso sistema, a memória de trabalho conteria a lista de **factos** como **pares atributo-valor**. A base de conhecimento pode ser definida como um grande dicionário que associa ações (novos factos que devem ser inseridos na memória de trabalho) a condições, expressas como expressões E-OU. Além disso, alguns factos podem ser `Perguntados`.\n" ] }, { @@ -99,13 +99,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Para realizar a inferência reversa, iremos definir a classe `Knowledgebase`. Ela conterá:\n", - "* `memória` de trabalho - um dicionário que mapeia atributos a valores\n", - "* `regras` da base de conhecimento no formato definido acima\n", + "Para realizar a inferência retrocedida, iremos definir a classe `Knowledgebase`. Esta conterá:\n", + "* Memória de trabalho `memory` - um dicionário que associa atributos a valores\n", + "* Regras da base de conhecimento `rules` no formato definido acima\n", "\n", "Os dois métodos principais são:\n", - "* `get` para obter o valor de um atributo, realizando a inferência, se necessário. Por exemplo, `get('color')` obteria o valor de um campo de cor (irá perguntar, se necessário, e armazenar o valor para uso posterior na memória de trabalho). Se pedirmos `get('color:blue')`, ele irá perguntar por uma cor e, em seguida, retornar o valor `y`/`n` dependendo da cor.\n", - "* `eval` realiza a inferência propriamente dita, ou seja, percorre a árvore AND/OR, avalia subobjetivos, etc.\n" + "* `get` para obter o valor de um atributo, realizando inferência se necessário. Por exemplo, `get('color')` obterá o valor de um campo de cor (irá perguntar se necessário, e guardar o valor para uso posterior na memória de trabalho). Se perguntarmos `get('color:blue')`, perguntará pela cor e depois retornará o valor `y`/`n` dependendo da cor.\n", + "* `eval` executa a inferência propriamente dita, ou seja, percorre a árvore AND/OR, avalia subobjetivos, etc.\n" ] }, { @@ -172,7 +172,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Agora vamos definir a nossa base de conhecimento sobre animais e realizar a consulta. Note que esta chamada irá fazer-lhe perguntas. Pode responder digitando `s`/`n` para perguntas de sim-não, ou especificando um número (0..N) para perguntas com respostas de múltipla escolha mais longas.\n" + "Agora, vamos definir a nossa base de conhecimento sobre animais e realizar a consulta. Note que esta chamada fará perguntas. Pode responder digitando `y`/`n` para perguntas de sim/não, ou especificando um número (0..N) para perguntas com respostas de múltipla escolha mais longas.\n" ] }, { @@ -229,11 +229,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Utilizar o PyKnow para Inferência Progressiva\n", + "## Usar o Experta para Inferência Direta\n", "\n", - "No próximo exemplo, vamos tentar implementar inferência progressiva utilizando uma das bibliotecas para representação de conhecimento, [PyKnow](https://github.com/buguroo/pyknow/). **PyKnow** é uma biblioteca para criar sistemas de inferência progressiva em Python, projetada para ser semelhante ao sistema clássico antigo [CLIPS](http://www.clipsrules.net/index.html).\n", + "No próximo exemplo, vamos tentar implementar inferência direta usando uma das bibliotecas para representação de conhecimento, o [Experta](https://github.com/nilp0inter/experta). **Experta** é uma biblioteca para criar sistemas de inferência direta em Python, que é desenhada para ser semelhante ao clássico sistema antigo [CLIPS](http://www.clipsrules.net/index.html).\n", "\n", - "Poderíamos também ter implementado encadeamento progressivo por conta própria sem grandes problemas, mas implementações ingênuas geralmente não são muito eficientes. Para um emparelhamento de regras mais eficaz, é utilizado um algoritmo especial chamado [Rete](https://en.wikipedia.org/wiki/Rete_algorithm).\n" + "Também poderíamos ter implementado encadeamento direto nós próprios sem muitos problemas, mas implementações ingênuas normalmente não são muito eficientes. Para uma correspondência de regras mais eficaz é usado um algoritmo especial [Rete](https://en.wikipedia.org/wiki/Rete_algorithm).\n" ] }, { @@ -247,32 +247,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "Collecting git+https://github.com/buguroo/pyknow/\n", - " Cloning https://github.com/buguroo/pyknow/ to /tmp/pip-req-build-3cqeulyl\n", - " Running command git clone --filter=blob:none --quiet https://github.com/buguroo/pyknow/ /tmp/pip-req-build-3cqeulyl\n", - " Resolved https://github.com/buguroo/pyknow/ to commit 48818336f2e9a126f1964f2d8dc22d37ff800fe8\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting frozendict==1.2\n", - " Using cached frozendict-1.2.tar.gz (2.6 kB)\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting schema==0.6.7\n", - " Using cached schema-0.6.7-py2.py3-none-any.whl (14 kB)\n", - "Building wheels for collected packages: pyknow, frozendict\n", - " Building wheel for pyknow (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for pyknow: filename=pyknow-1.7.0-py3-none-any.whl size=34228 sha256=b7de5b09292c4007667c72f69b98d5a1b5f7324ff15f9dd8e077c3d5f7aade42\n", - " Stored in directory: /tmp/pip-ephem-wheel-cache-k7jpave7/wheels/81/1a/d3/f6c15dbe1955598a37755215f2a10449e7418500d7bd4b9508\n", - " Building wheel for frozendict (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for frozendict: filename=frozendict-1.2-py3-none-any.whl size=3148 sha256=2863d55c240d2409cddf05ccfe600591f8478681549fc97555c47c90dc6bb160\n", - " Stored in directory: /home/rg/.cache/pip/wheels/49/ac/f8/cb8120244e710bdb479c86198b03c7b08c3c2d3d2bf448fd6e\n", - "Successfully built pyknow frozendict\n", - "Installing collected packages: schema, frozendict, pyknow\n", - "Successfully installed frozendict-1.2 pyknow-1.7.0 schema-0.6.7\n" + "Collecting git+https://github.com/nilp0inter/experta\n", + " Cloning https://github.com/nilp0inter/experta to /tmp/pip-req-build-7qurtwk3\n", + " Running command git clone --filter=blob:none --quiet https://github.com/nilp0inter/experta /tmp/pip-req-build-7qurtwk3\n", + " Resolved https://github.com/nilp0inter/experta to commit c6d5834b123861f5ae09e7d07027dc98bec58741\n", + " Installing build dependencies ... \u001b[?25ldone\n", + "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", + "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25hRequirement already satisfied: frozendict~=2.4.6 in /opt/conda/envs/ai4beg/lib/python3.12/site-packages (from experta==1.9.5.dev1) (2.4.7)\n", + "Collecting schema~=0.6.7 (from experta==1.9.5.dev1)\n", + " Downloading schema-0.6.8-py2.py3-none-any.whl.metadata (14 kB)\n", + "Downloading schema-0.6.8-py2.py3-none-any.whl (14 kB)\n", + "Building wheels for collected packages: experta\n", + " Building wheel for experta (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25h Created wheel for experta: filename=experta-1.9.5.dev1-py3-none-any.whl size=34804 sha256=888c459512a5e713f4b674caa9a0f96cfdf07ec0d6eb56cc318ce0653d218014\n", + " Stored in directory: /tmp/pip-ephem-wheel-cache-1eeii9zy/wheels/3d/e8/bb/22d7956359603fa8dd679aa09f5b8efb3f29991c3986fdc787\n", + "Successfully built experta\n", + "Installing collected packages: schema, experta\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2/2\u001b[0m [experta]\n", + "\u001b[1A\u001b[2KSuccessfully installed experta-1.9.5.dev1 schema-0.6.8\n" ] } ], "source": [ "import sys\n", - "!{sys.executable} -m pip install git+https://github.com/buguroo/pyknow/" + "!{sys.executable} -m pip install git+https://github.com/nilp0inter/experta" ] }, { @@ -283,15 +282,15 @@ }, "outputs": [], "source": [ - "from pyknow import *\n", - "#import pyknow" + "from experta import *\n", + "#import experta" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Definiremos o nosso sistema como uma classe que herda de `KnowledgeEngine`. Cada regra é definida por uma função separada com a anotação `@Rule`, que especifica quando a regra deve ser acionada. Dentro da regra, podemos adicionar novos factos usando a função `declare`, e adicionar esses factos resultará em mais regras serem chamadas pelo motor de inferência direta.\n" + "Iremos definir o nosso sistema como uma classe que herda de `KnowledgeEngine`. Cada regra é definida por uma função separada com a anotação `@Rule`, que especifica quando a regra deve ser ativada. Dentro da regra, podemos adicionar novos factos utilizando a função `declare`, e adicionar esses factos resultará em algumas regras adicionais serem acionadas pelo motor de inferência para a frente.\n" ] }, { @@ -378,7 +377,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Assim que definimos uma base de conhecimento, populamos a nossa memória de trabalho com alguns factos iniciais e, em seguida, chamamos o método `run()` para realizar a inferência. Pode ver, como resultado, que novos factos inferidos são adicionados à memória de trabalho, incluindo o facto final sobre o animal (se configurarmos todos os factos iniciais corretamente).\n" + "Depois de definirmos uma base de conhecimento, preenchemos a nossa memória de trabalho com alguns factos iniciais e, em seguida, chamamos o método `run()` para realizar a inferência. Pode ver-se como resultado que novos factos inferidos são adicionados à memória de trabalho, incluindo o facto final sobre o animal (se configurarmos correctamente todos os factos iniciais).\n" ] }, { @@ -440,7 +439,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n---\n\n**Aviso Legal**: \nEste documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, é importante ter em conta que traduções automáticas podem conter erros ou imprecisões. O documento original na sua língua nativa deve ser considerado a fonte autoritária. Para informações críticas, recomenda-se a tradução profissional realizada por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações incorretas decorrentes da utilização desta tradução.\n" + "---\n\n\n**Aviso Legal**: \nEste documento foi traduzido utilizando o serviço de tradução automática [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, por favor tenha em conta que traduções automáticas podem conter erros ou imprecisões. O documento original na sua língua nativa deve ser considerado a fonte oficial. Para informações críticas, recomenda-se tradução profissional feita por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações incorretas decorrentes da utilização desta tradução.\n\n" ] } ], @@ -467,8 +466,8 @@ "version": "3.11.2" }, "coopTranslator": { - "original_hash": "ab2bd97b0453415b89a469284609a8ce", - "translation_date": "2025-08-31T11:48:17+00:00", + "original_hash": "8ef43db4b9182239fd150a76bd494fdb", + "translation_date": "2026-01-15T14:03:23+00:00", "source_file": "lessons/2-Symbolic/Animals.ipynb", "language_code": "pt" } diff --git a/translations/pt/lessons/2-Symbolic/README.md b/translations/pt/lessons/2-Symbolic/README.md index f579538a..4b4b6e6e 100644 --- a/translations/pt/lessons/2-Symbolic/README.md +++ b/translations/pt/lessons/2-Symbolic/README.md @@ -1,116 +1,116 @@ -# Representação de Conhecimento e Sistemas Especialistas +# Representação do Conhecimento e Sistemas Especialistas -![Resumo do conteúdo de IA Simbólica](../../../../translated_images/pt/ai-symbolic.715a30cb610411a6.webp) +![Resumo do conteúdo de IA Simbólica](../../../../../../translated_images/pt/ai-symbolic.715a30cb610411a6.webp) > Sketchnote por [Tomomi Imura](https://twitter.com/girlie_mac) -A busca pela inteligência artificial baseia-se na procura por conhecimento, para compreender o mundo de forma semelhante aos humanos. Mas como é possível fazer isso? +A busca pela inteligência artificial baseia-se na procura por conhecimento, para compreender o mundo de forma semelhante aos humanos. Mas como é que se pode fazer isso? ## [Questionário pré-aula](https://ff-quizzes.netlify.app/en/ai/quiz/3) -Nos primeiros dias da IA, a abordagem de cima para baixo para criar sistemas inteligentes (discutida na aula anterior) era popular. A ideia era extrair o conhecimento das pessoas para uma forma legível por máquinas e, em seguida, utilizá-lo para resolver problemas automaticamente. Esta abordagem baseava-se em duas grandes ideias: +Nos primeiros tempos da IA, a abordagem top-down para criar sistemas inteligentes (discutida na aula anterior) era popular. A ideia era extrair o conhecimento das pessoas para alguma forma legível por máquina, e depois usá-lo para resolver problemas automaticamente. Esta abordagem baseava-se em duas grandes ideias: -* Representação de Conhecimento -* Raciocínio +* Representação do Conhecimento +* Raciocínio -## Representação de Conhecimento +## Representação do Conhecimento -Um dos conceitos importantes na IA Simbólica é o **conhecimento**. É essencial diferenciar conhecimento de *informação* ou *dados*. Por exemplo, pode-se dizer que os livros contêm conhecimento, porque ao estudá-los podemos tornar-nos especialistas. No entanto, o que os livros realmente contêm é chamado de *dados*, e ao ler os livros e integrar esses dados no nosso modelo do mundo, convertemos os dados em conhecimento. +Um dos conceitos importantes na IA Simbólica é o **conhecimento**. É importante diferenciar conhecimento de *informação* ou *dados*. Por exemplo, pode-se dizer que os livros contêm conhecimento, porque se pode estudar livros e tornar-se perito. No entanto, o que os livros realmente contêm são chamados *dados*, e ao ler livros e integrar esses dados no nosso modelo do mundo, convertemos esses dados em conhecimento. -> ✅ **Conhecimento** é algo que está na nossa mente e representa a nossa compreensão do mundo. É obtido através de um processo ativo de **aprendizagem**, que integra pedaços de informação que recebemos no nosso modelo ativo do mundo. +> ✅ **Conhecimento** é algo que está contido na nossa cabeça e representa a nossa compreensão do mundo. É obtido por um processo ativo de **aprendizagem**, que integra peças de informação que recebemos no nosso modelo ativo do mundo. -Na maioria das vezes, não definimos estritamente o conhecimento, mas alinhamos com outros conceitos relacionados usando a [Pirâmide DIKW](https://en.wikipedia.org/wiki/DIKW_pyramid). Ela contém os seguintes conceitos: +Na maior parte das vezes, não definimos conhecimento de forma rigorosa, mas alinhamos com outros conceitos relacionados usando a [Pirâmide DIKW](https://en.wikipedia.org/wiki/DIKW_pyramid). Esta contém os seguintes conceitos: -* **Dados** são algo representado em meios físicos, como texto escrito ou palavras faladas. Os dados existem independentemente dos seres humanos e podem ser transmitidos entre pessoas. -* **Informação** é como interpretamos os dados na nossa mente. Por exemplo, ao ouvir a palavra *computador*, temos alguma compreensão do que é. -* **Conhecimento** é a informação integrada no nosso modelo do mundo. Por exemplo, ao aprender o que é um computador, começamos a ter ideias sobre como funciona, quanto custa e para que pode ser usado. Esta rede de conceitos inter-relacionados forma o nosso conhecimento. -* **Sabedoria** é um nível ainda mais elevado da nossa compreensão do mundo, representando o *meta-conhecimento*, ou seja, uma noção de como e quando o conhecimento deve ser usado. +* **Dados** são algo representado em suporte físico, como texto escrito ou palavras faladas. Os dados existem independentemente dos seres humanos e podem ser passados entre pessoas. +* **Informação** é como interpretamos os dados na nossa mente. Por exemplo, quando ouvimos a palavra *computador*, temos alguma compreensão do que é. +* **Conhecimento** é a informação integrada no nosso modelo do mundo. Por exemplo, uma vez que aprendemos o que é um computador, começamos a ter algumas ideias sobre como funciona, quanto custa e para que pode ser usado. Esta rede de conceitos inter-relacionados forma o nosso conhecimento. +* **Sabedoria** é mais um nível da nossa compreensão do mundo, representando *meta-conhecimento*, por exemplo, alguma noção de como e quando o conhecimento deve ser utilizado. - + -*Imagem [da Wikipedia](https://commons.wikimedia.org/w/index.php?curid=37705247), Por Longlivetheux - Trabalho próprio, CC BY-SA 4.0* +*Imagem [da Wikipedia](https://commons.wikimedia.org/w/index.php?curid=37705247), por Longlivetheux - Trabalho próprio, CC BY-SA 4.0* -Assim, o problema da **representação de conhecimento** é encontrar uma forma eficaz de representar o conhecimento dentro de um computador na forma de dados, para torná-lo automaticamente utilizável. Isso pode ser visto como um espectro: +Assim, o problema da **representação do conhecimento** é encontrar uma forma eficaz de representar conhecimento dentro de um computador sob a forma de dados, para que possa ser utilizado automaticamente. Isto pode ser visto como um espectro: -![Espectro de representação de conhecimento](../../../../translated_images/pt/knowledge-spectrum.b60df631852c0217.webp) +![Espectro da representação do conhecimento](../../../../../../translated_images/pt/knowledge-spectrum.b60df631852c0217.webp) > Imagem por [Dmitry Soshnikov](http://soshnikov.com) -* À esquerda, há tipos muito simples de representações de conhecimento que podem ser usados de forma eficaz por computadores. O mais simples é o algorítmico, onde o conhecimento é representado por um programa de computador. No entanto, esta não é a melhor forma de representar conhecimento, pois não é flexível. O conhecimento na nossa mente é frequentemente não algorítmico. -* À direita, há representações como texto natural. É a mais poderosa, mas não pode ser usada para raciocínio automático. +* À esquerda, existem tipos muito simples de representações de conhecimento que podem ser eficazmente usados por computadores. A mais simples é a algorítmica, quando o conhecimento é representado por um programa de computador. No entanto, esta não é a melhor forma de representar conhecimento, porque não é flexível. O conhecimento dentro da nossa cabeça é frequentemente não algorítmico. +* À direita, existem representações como texto natural. É a mais poderosa, mas não pode ser usada para raciocínio automático. -> ✅ Pense por um momento sobre como você representa conhecimento na sua mente e o converte em notas. Existe algum formato específico que funciona bem para ajudar na retenção? +> ✅ Pense por um minuto em como representa o conhecimento na sua cabeça e o converte em notas. Existe algum formato particular que funcione bem para ajudar na retenção? -## Classificação de Representações de Conhecimento em Computadores +## Classificação dos Métodos de Representação de Conhecimento em Computadores -Podemos classificar diferentes métodos de representação de conhecimento em computadores nas seguintes categorias: +Podemos classificar os diferentes métodos de representação de conhecimento computacional nas seguintes categorias: -* **Representações em rede** baseiam-se no fato de que temos uma rede de conceitos inter-relacionados na nossa mente. Podemos tentar reproduzir essas redes como um grafo dentro de um computador - uma chamada **rede semântica**. +* **Representações em rede** baseiam-se no facto de termos uma rede de conceitos inter-relacionados dentro da nossa cabeça. Podemos tentar reproduzir as mesmas redes como um grafo dentro de um computador – a chamada **rede semântica**. -1. **Triplas Objeto-Atributo-Valor** ou **pares atributo-valor**. Como um grafo pode ser representado dentro de um computador como uma lista de nós e arestas, podemos representar uma rede semântica por uma lista de triplas, contendo objetos, atributos e valores. Por exemplo, construímos as seguintes triplas sobre linguagens de programação: +1. **Triplos Objeto-Atributo-Valor** ou **pares atributo-valor**. Como um grafo pode ser representado dentro de um computador como uma lista de nós e ligações, podemos representar uma rede semântica por uma lista de triplos, contendo objetos, atributos e valores. Por exemplo, criamos os seguintes triplos sobre linguagens de programação: -Objeto | Atributo | Valor --------|----------|------ -Python | é | Linguagem Não Tipada -Python | inventado-por | Guido van Rossum -Python | sintaxe-de-bloco | indentação -Linguagem Não Tipada | não tem | definições de tipo +Objeto | Atributo | Valor +-------|----------|------- +Python | é | Linguagem Não Tipada +Python | inventado-por | Guido van Rossum +Python | sintaxe-bloco | indentação +Linguagem Não Tipada | não tem | definições de tipo -> ✅ Pense como as triplas podem ser usadas para representar outros tipos de conhecimento. +> ✅ Pense como os triplos podem ser usados para representar outros tipos de conhecimento. -2. **Representações hierárquicas** enfatizam o fato de que frequentemente criamos uma hierarquia de objetos na nossa mente. Por exemplo, sabemos que o canário é um pássaro, e todos os pássaros têm asas. Também temos alguma ideia sobre a cor de um canário e a sua velocidade de voo. +2. **Representações hierárquicas** enfatizam o facto de criarmos frequentemente uma hierarquia de objetos dentro da nossa cabeça. Por exemplo, sabemos que canário é um pássaro, e que todos os pássaros têm asas. Também temos alguma ideia da cor que um canário normalmente tem e da sua velocidade de voo. - - **Representação por quadros** baseia-se em representar cada objeto ou classe de objetos como um **quadro** que contém **slots**. Os slots têm valores padrão possíveis, restrições de valor ou procedimentos armazenados que podem ser chamados para obter o valor de um slot. Todos os quadros formam uma hierarquia semelhante à hierarquia de objetos em linguagens de programação orientadas a objetos. - - **Cenários** são um tipo especial de quadros que representam situações complexas que podem se desenrolar ao longo do tempo. + - **Representação por quadros** baseia-se em representar cada objeto ou classe de objetos como um **quadro** que contém **espaços (slots)**. Os espaços têm valores padrão possíveis, restrições de valor ou procedimentos armazenados que podem ser chamados para obter o valor de um espaço. Todos os quadros formam uma hierarquia semelhante a uma hierarquia de objetos em linguagens de programação orientadas a objetos. + - **Cenários** são um tipo especial de quadros que representam situações complexas que podem desenvolver-se no tempo. **Python** -Slot | Valor | Valor padrão | Intervalo ------|-------|--------------|---------- -Nome | Python | | -É-Um | Linguagem Não Tipada | | -Caso de Variável | | CamelCase | -Comprimento do Programa | | | 5-5000 linhas -Sintaxe de Bloco | Indentação | | +Espaço | Valor | Valor padrão | Intervalo | +-------|-------|--------------|-----------| +Nome | Python | | | +É-Um | Linguagem Não Tipada | | | +Caso Variável | | CamelCase | | +Comprimento do Programa | | | 5-5000 linhas | +Sintaxe de Bloco | Indentação | | | -3. **Representações procedurais** baseiam-se em representar conhecimento por uma lista de ações que podem ser executadas quando uma certa condição ocorre. - - Regras de produção são declarações do tipo se-então que nos permitem tirar conclusões. Por exemplo, um médico pode ter uma regra dizendo que **SE** um paciente tem febre alta **OU** um nível elevado de proteína C-reativa no exame de sangue **ENTÃO** ele tem uma inflamação. Quando encontramos uma das condições, podemos concluir sobre a inflamação e, em seguida, usá-la em raciocínios posteriores. - - Algoritmos podem ser considerados outra forma de representação procedural, embora quase nunca sejam usados diretamente em sistemas baseados em conhecimento. +3. **Representações procedurais** são baseadas em representar conhecimento por uma lista de ações que podem ser executadas quando uma certa condição ocorre. + - Regras de produção são sentenças do tipo se-então que permitem tirar conclusões. Por exemplo, um médico pode ter uma regra que diz que **SE** um paciente tem febre alta **OU** nível elevado de proteína C-reativa no exame sanguíneo **ENTÃO** ele tem uma inflamação. Uma vez que surge uma das condições, podemos concluir a inflamação, e depois usar isso em raciocínios posteriores. + - Algoritmos podem ser considerados outra forma de representação procedural, embora quase nunca sejam usados diretamente em sistemas baseados em conhecimento. -4. **Lógica** foi originalmente proposta por Aristóteles como uma forma de representar o conhecimento humano universal. - - A Lógica de Predicados como teoria matemática é muito rica para ser computável, portanto, normalmente é usado algum subconjunto dela, como cláusulas de Horn usadas em Prolog. - - A Lógica Descritiva é uma família de sistemas lógicos usados para representar e raciocinar sobre hierarquias de objetos em representações de conhecimento distribuído, como a *web semântica*. +4. **Lógica** foi originalmente proposta por Aristóteles como uma forma de representar o conhecimento universal humano. + - A Lógica de Predicados como teoria matemática é demasiado rica para ser computável, portanto usa-se normalmente algum subconjunto dela, como as cláusulas de Horn usadas em Prolog. + - A Lógica Descritiva é uma família de sistemas lógicos usados para representar e raciocinar sobre hierarquias de objetos em representações de conhecimento distribuído, como a *web semântica*. ## Sistemas Especialistas -Um dos primeiros sucessos da IA simbólica foram os chamados **sistemas especialistas** - sistemas computacionais projetados para atuar como especialistas em um domínio de problema limitado. Eles baseavam-se em uma **base de conhecimento** extraída de um ou mais especialistas humanos e continham um **motor de inferência** que realizava algum raciocínio sobre ela. +Um dos primeiros sucessos da IA simbólica foram os chamados **sistemas especialistas** – sistemas computacionais desenhados para agir como um especialista num domínio de problema limitado. Eram baseados numa **base de conhecimento** extraída de um ou mais especialistas humanos, e continham um **mecanismo de inferência** que realizava algum raciocínio sobre ela. -![Arquitetura Humana](../../../../translated_images/pt/arch-human.5d4d35f1bba3ab1c.webp) | ![Sistema Baseado em Conhecimento](../../../../translated_images/pt/arch-kbs.3ec5c150b09fa8da.webp) ----------------------------------------------|------------------------------------------------ -Estrutura simplificada do sistema neural humano | Arquitetura de um sistema baseado em conhecimento +![Arquitetura Humana](../../../../../../translated_images/pt/arch-human.5d4d35f1bba3ab1c.webp) | ![Sistema Baseado em Conhecimento](../../../../../../translated_images/pt/arch-kbs.3ec5c150b09fa8da.webp) +---------------------------------------------|----------------------------------------------- +Estrutura simplificada do sistema neural humano | Arquitetura de um sistema baseado em conhecimento -Os sistemas especialistas são construídos como o sistema de raciocínio humano, que contém **memória de curto prazo** e **memória de longo prazo**. Da mesma forma, nos sistemas baseados em conhecimento distinguimos os seguintes componentes: +Os sistemas especialistas são construídos como o sistema de raciocínio humano, que contém **memória de curto prazo** e **memória de longo prazo**. Igualmente, em sistemas baseados em conhecimento distinguimos os seguintes componentes: -* **Memória do problema**: contém o conhecimento sobre o problema que está sendo resolvido no momento, ou seja, a temperatura ou pressão arterial de um paciente, se ele tem inflamação ou não, etc. Este conhecimento também é chamado de **conhecimento estático**, porque contém um instantâneo do que sabemos atualmente sobre o problema - o chamado *estado do problema*. -* **Base de conhecimento**: representa o conhecimento de longo prazo sobre um domínio de problema. É extraído manualmente de especialistas humanos e não muda de consulta para consulta. Como permite navegar de um estado do problema para outro, também é chamado de **conhecimento dinâmico**. -* **Motor de inferência**: orquestra todo o processo de busca no espaço de estados do problema, fazendo perguntas ao usuário quando necessário. Também é responsável por encontrar as regras certas para serem aplicadas a cada estado. +* **Memória do problema**: contém o conhecimento sobre o problema que está a ser atualmente resolvido, isto é, a temperatura ou pressão arterial de um paciente, se tem inflamação ou não, etc. Este conhecimento é também chamado **conhecimento estático**, porque contém uma fotografia do que sabemos atualmente sobre o problema – o chamado *estado do problema*. +* **Base de conhecimento**: representa o conhecimento de longo prazo sobre um domínio de problema. É extraída manualmente de especialistas humanos e não muda de consulta para consulta. Porque permite navegar de um estado do problema para outro, também é chamada **conhecimento dinâmico**. +* **Mecanismo de inferência**: orquestra todo o processo de busca no espaço de estados do problema, fazendo perguntas ao utilizador quando necessário. É também responsável por encontrar as regras certas a aplicar em cada estado. -Como exemplo, vamos considerar o seguinte sistema especialista para determinar um animal com base nas suas características físicas: +Como exemplo, vamos considerar o seguinte sistema especialista para determinar um animal baseado nas suas características físicas: -![Árvore AND-OR](../../../../translated_images/pt/AND-OR-Tree.5592d2c70187f283.webp) +![Árvore AND-OR](../../../../../../translated_images/pt/AND-OR-Tree.5592d2c70187f283.webp) > Imagem por [Dmitry Soshnikov](http://soshnikov.com) -Este diagrama é chamado de **árvore AND-OR**, e é uma representação gráfica de um conjunto de regras de produção. Desenhar uma árvore é útil no início da extração de conhecimento do especialista. Para representar o conhecimento dentro do computador, é mais conveniente usar regras: +Este diagrama é chamado de **árvore AND-OR**, e é uma representação gráfica de um conjunto de regras de produção. Desenhar uma árvore é útil no início da extração do conhecimento do especialista. Para representar o conhecimento dentro do computador, é mais conveniente usar regras: ``` IF the animal eats meat @@ -120,65 +120,65 @@ OR (animal has sharp teeth ) THEN the animal is a carnivore ``` - -Você pode notar que cada condição no lado esquerdo da regra e a ação são essencialmente triplas objeto-atributo-valor (OAV). A **memória de trabalho** contém o conjunto de triplas OAV que correspondem ao problema que está sendo resolvido no momento. Um **motor de regras** procura regras cujas condições são satisfeitas e as aplica, adicionando outra tripla à memória de trabalho. -> ✅ Escreva sua própria árvore AND-OR sobre um tema que você goste! +Pode notar que cada condição no lado esquerdo da regra e a ação são essencialmente triplos objeto-atributo-valor (OAV). A **memória de trabalho** contém o conjunto de triplos OAV que correspondem ao problema atualmente a resolver. Um **motor de regras** procura regras cuja condição seja satisfeita e aplica-as, adicionando outro triplo à memória de trabalho. -### Inferência Progressiva vs. Regressiva +> ✅ Escreva a sua própria árvore AND-OR sobre um tema do seu interesse! -O processo descrito acima é chamado de **inferência progressiva**. Ele começa com alguns dados iniciais sobre o problema disponíveis na memória de trabalho e, em seguida, executa o seguinte ciclo de raciocínio: +### Inferência Direta vs. Inferência Retroativa -1. Se o atributo alvo estiver presente na memória de trabalho - pare e forneça o resultado -2. Procure todas as regras cujas condições estão atualmente satisfeitas - obtenha o **conjunto de conflito** de regras. -3. Realize a **resolução de conflito** - selecione uma regra que será executada nesta etapa. Podem existir diferentes estratégias de resolução de conflito: - - Selecionar a primeira regra aplicável na base de conhecimento - - Selecionar uma regra aleatória - - Selecionar uma regra *mais específica*, ou seja, aquela que atende ao maior número de condições no lado esquerdo ("LHS") -4. Aplicar a regra selecionada e inserir um novo pedaço de conhecimento no estado do problema -5. Repetir a partir do passo 1. +O processo descrito acima chama-se **inferência direta**. Começa com alguns dados iniciais sobre o problema disponíveis na memória de trabalho e depois executa o seguinte ciclo de raciocínio: -No entanto, em alguns casos, podemos querer começar com um conhecimento vazio sobre o problema e fazer perguntas que nos ajudem a chegar à conclusão. Por exemplo, ao fazer um diagnóstico médico, geralmente não realizamos todas as análises médicas antecipadamente antes de começar a diagnosticar o paciente. Preferimos realizar análises quando uma decisão precisa ser tomada. +1. Se o atributo alvo está presente na memória de trabalho – parar e fornecer o resultado +2. Procurar todas as regras cuja condição está atualmente satisfeita – obter o **conjunto de conflito** das regras. +3. Realizar a **resolução de conflito** – selecionar uma regra que será executada neste passo. Podem existir diferentes estratégias de resolução de conflito: + - Selecionar a primeira regra aplicável na base de conhecimento + - Selecionar uma regra aleatória + - Selecionar uma regra *mais específica*, ou seja, aquela que satisfaz mais condições no "lado esquerdo" (LHS) +4. Aplicar a regra selecionada e inserir uma nova peça de conhecimento no estado do problema +5. Repetir desde o passo 1. -Este processo pode ser modelado usando **inferência regressiva**. Ele é orientado pelo **objetivo** - o valor do atributo que estamos tentando encontrar: +No entanto, em alguns casos talvez queiramos começar com conhecimento vazio sobre o problema, e fazer perguntas que nos ajudem a chegar à conclusão. Por exemplo, na realização de um diagnóstico médico, normalmente não fazemos todos os exames médicos antecipadamente antes de começar a diagnosticar o paciente. Preferimos realizar exames quando é necessário tomar uma decisão. -1. Selecionar todas as regras que podem nos dar o valor de um objetivo (ou seja, com o objetivo no lado direito ("RHS")) - um conjunto de conflito -1. Se não houver regras para este atributo, ou houver uma regra dizendo que devemos perguntar o valor ao usuário - pergunte, caso contrário: -1. Use a estratégia de resolução de conflito para selecionar uma regra que usaremos como *hipótese* - tentaremos prová-la -1. Repetir recursivamente o processo para todos os atributos no LHS da regra, tentando prová-los como objetivos -1. Se em algum momento o processo falhar - use outra regra no passo 3. +Este processo pode ser modelado usando **inferência retroativa**. É conduzido pelo **objetivo** – o valor do atributo que estamos a tentar encontrar: -> ✅ Em quais situações a inferência progressiva é mais apropriada? E a inferência regressiva? +1. Selecionar todas as regras que possam dar-nos o valor do objetivo (ou seja, com o objetivo no lado direito (RHS)) – um conjunto de conflito +1. Se não houver regras para este atributo, ou houver uma regra que diga que devemos perguntar o valor ao utilizador – perguntar, caso contrário: +1. Usar a estratégia de resolução de conflito para selecionar uma regra que vamos usar como *hipótese* – vamos tentar prová-la +1. Repetir recursivamente o processo para todos os atributos no LHS da regra, tentando prová-los como objetivos +1. Se em algum momento o processo falhar – usar outra regra no passo 3. + +> ✅ Em que situações a inferência direta é mais apropriada? E a inferência retroativa? ### Implementação de Sistemas Especialistas -Os sistemas especialistas podem ser implementados usando diferentes ferramentas: +Sistemas especialistas podem ser implementados usando diferentes ferramentas: -* Programá-los diretamente em alguma linguagem de programação de alto nível. Esta não é a melhor ideia, porque a principal vantagem de um sistema baseado em conhecimento é que o conhecimento é separado da inferência, e potencialmente um especialista no domínio do problema deve ser capaz de escrever regras sem entender os detalhes do processo de inferência. -* Usar um **shell de sistemas especialistas**, ou seja, um sistema especificamente projetado para ser preenchido com conhecimento usando alguma linguagem de representação de conhecimento. +* Programando-os diretamente numa linguagem de programação de alto nível. Esta não é a melhor ideia, porque a principal vantagem de um sistema baseado em conhecimento é que o conhecimento está separado da inferência, e potencialmente um especialista do domínio do problema deve poder escrever regras sem entender os detalhes do processo de inferência +* Usando **shells de sistemas especialistas**, ou seja, sistemas especificamente desenhados para serem populados com conhecimento usando alguma linguagem de representação do conhecimento. ## ✍️ Exercício: Inferência de Animais -Veja [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) para um exemplo de implementação de sistema especialista com inferência progressiva e regressiva. +Veja [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) para um exemplo da implementação de um sistema especialista com inferência direta e retroativa. -> **Nota**: Este exemplo é bastante simples e apenas dá uma ideia de como é um sistema especialista. Quando começar a criar um sistema assim, só perceberá algum comportamento *inteligente* dele quando atingir um certo número de regras, cerca de 200+. Em algum momento, as regras tornam-se muito complexas para manter todas na mente, e nesse ponto pode começar a se perguntar por que o sistema toma certas decisões. No entanto, a característica importante dos sistemas baseados em conhecimento é que você pode sempre *explicar* exatamente como qualquer decisão foi tomada. +> **Nota**: Este exemplo é bastante simples e apenas dá uma ideia de como é um sistema especialista. Depois de começar a criar um sistema assim, só notará algum comportamento *inteligente* quando atingir um certo número de regras, cerca de 200+. Em algum ponto, as regras tornam-se demasiado complexas para manter todas em mente, e nesse ponto pode começar a perguntar-se porque é que um sistema toma certas decisões. Contudo, a característica importante dos sistemas baseados em conhecimento é que pode sempre *explicar* exatamente como qualquer decisão foi tomada. ## Ontologias e a Web Semântica -No final do século XX, houve uma iniciativa para usar a representação de conhecimento para anotar recursos da Internet, de modo que fosse possível encontrar recursos que correspondam a consultas muito específicas. Este movimento foi chamado de **Web Semântica**, e baseava-se em vários conceitos: +No final do século XX houve uma iniciativa para usar a representação de conhecimento para anotar recursos na Internet, de forma a ser possível encontrar recursos que correspondam a pesquisas muito específicas. Esta iniciativa foi chamada **Web Semântica**, e baseava-se em vários conceitos: -- Uma representação de conhecimento especial baseada em **[lógicas descritivas](https://en.wikipedia.org/wiki/Description_logic)** (DL). É semelhante à representação por quadros, porque constrói uma hierarquia de objetos com propriedades, mas tem semântica lógica formal e inferência. Existe toda uma família de DLs que equilibram entre expressividade e complexidade algorítmica da inferência. -- Representação de conhecimento distribuído, onde todos os conceitos são representados por um identificador global URI, tornando possível criar hierarquias de conhecimento que abrangem a internet. +- Uma representação especial do conhecimento baseada em **[lógicas descritivas](https://en.wikipedia.org/wiki/Description_logic)** (DL). É semelhante à representação de conhecimento por quadros, porque constrói uma hierarquia de objetos com propriedades, mas tem semântica formal lógica e inferência. Existe uma família inteira de DLs que equilibram a expressividade e a complexidade algorítmica da inferência. +- Representação de conhecimento distribuído, onde todos os conceitos são representados por um identificador global URI, tornando possível criar hierarquias de conhecimento que abrangem a internet. - Uma família de linguagens baseadas em XML para descrição de conhecimento: RDF (Resource Description Framework), RDFS (RDF Schema), OWL (Ontology Web Language). -Um conceito central na Web Semântica é o conceito de **Ontologia**. Refere-se a uma especificação explícita de um domínio de problema utilizando alguma representação formal de conhecimento. A ontologia mais simples pode ser apenas uma hierarquia de objetos num domínio de problema, mas ontologias mais complexas incluirão regras que podem ser usadas para inferência. +Um conceito central na Web Semântica é o conceito de **Ontologia**. Refere-se à especificação explícita de um domínio de problema usando alguma representação formal de conhecimento. A ontologia mais simples pode ser apenas uma hierarquia de objetos em um domínio de problema, mas ontologias mais complexas incluirão regras que podem ser usadas para inferência. -Na Web Semântica, todas as representações são baseadas em triplos. Cada objeto e cada relação são identificados de forma única pelo URI. Por exemplo, se quisermos afirmar o facto de que este Currículo de IA foi desenvolvido por Dmitry Soshnikov a 1 de janeiro de 2022 - aqui estão os triplos que podemos usar: +Na web semântica, todas as representações são baseadas em triplets. Cada objeto e cada relação são identificados unicamente pelo URI. Por exemplo, se quisermos declarar o facto de que este Currículo de IA foi desenvolvido por Dmitry Soshnikov em 1 de Janeiro de 2022 - aqui estão os triplets que podemos usar: - + ``` -http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 13, 2007” +http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 1, 2022” http://github.com/microsoft/ai-for-beginners http://purl.org/dc/elements/1.1/creator http://soshnikov.com ``` @@ -186,13 +186,13 @@ http://github.com/microsoft/ai-for-beginners http://purl.org/dc/elements/1.1/cre Num caso mais complexo, se quisermos definir uma lista de criadores, podemos usar algumas estruturas de dados definidas em RDF. - + > Diagramas acima por [Dmitry Soshnikov](http://soshnikov.com) -O progresso na construção da Web Semântica foi de certa forma desacelerado pelo sucesso dos motores de busca e das técnicas de processamento de linguagem natural, que permitem extrair dados estruturados de texto. No entanto, em algumas áreas ainda há esforços significativos para manter ontologias e bases de conhecimento. Alguns projetos que merecem destaque: +O progresso na construção da Web Semântica foi de algum modo travado pelo sucesso dos motores de busca e das técnicas de processamento de linguagem natural, que permitem extrair dados estruturados a partir de texto. No entanto, em algumas áreas ainda existem esforços significativos para manter ontologias e bases de conhecimento. Alguns projetos dignos de nota: -* [WikiData](https://wikidata.org/) é uma coleção de bases de conhecimento legíveis por máquinas associadas à Wikipedia. A maior parte dos dados é extraída das *InfoBoxes* da Wikipedia, pedaços de conteúdo estruturado dentro das páginas da Wikipedia. Pode [consultar](https://query.wikidata.org/) o WikiData em SPARQL, uma linguagem de consulta especial para a Web Semântica. Aqui está uma consulta de exemplo que mostra as cores de olhos mais populares entre os humanos: +* [WikiData](https://wikidata.org/) é uma coleção de bases de conhecimento legíveis por máquina associadas à Wikipédia. A maior parte dos dados é extraída das *InfoBoxes* da Wikipédia, que são pedaços de conteúdo estruturado dentro das páginas da Wikipédia. Pode [consultar](https://query.wikidata.org/) o wikidata em SPARQL, uma linguagem de consulta especial para a Web Semântica. Aqui está uma consulta de exemplo que mostra as cores de olhos mais populares entre humanos: ```sparql #defaultView:BubbleChart @@ -208,45 +208,50 @@ GROUP BY ?eyeColorLabel * [DBpedia](https://www.dbpedia.org/) é outro esforço semelhante ao WikiData. -> ✅ Se quiser experimentar construir as suas próprias ontologias ou abrir ontologias existentes, há um excelente editor visual de ontologias chamado [Protégé](https://protege.stanford.edu/). Faça o download ou use online. +> ✅ Se quiser experimentar criar as suas próprias ontologias, ou abrir ontologias existentes, existe um excelente editor visual de ontologias chamado [Protégé](https://protege.stanford.edu/). Faça o download ou use online. - + *Editor Web Protégé aberto com a ontologia da Família Romanov. Captura de ecrã por Dmitry Soshnikov* -## ✍️ Exercício: Uma Ontologia Familiar +## ✍️ Exercício: Uma Ontologia de Família -Veja [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) para um exemplo de utilização de técnicas da Web Semântica para raciocinar sobre relações familiares. Vamos pegar numa árvore genealógica representada no formato comum GEDCOM e numa ontologia de relações familiares e construir um gráfico de todas as relações familiares para um conjunto de indivíduos dado. + +Consulte [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) para um exemplo de uso de técnicas da Web Semântica para raciocinar sobre relações familiares. Vamos pegar numa árvore genealógica representada no formato comum GEDCOM e numa ontologia de relações familiares e construir um grafo de todas as relações familiares para um dado conjunto de indivíduos. ## Microsoft Concept Graph -Na maioria dos casos, as ontologias são cuidadosamente criadas manualmente. No entanto, também é possível **extrair** ontologias de dados não estruturados, por exemplo, de textos em linguagem natural. +Na maioria dos casos, as ontologias são cuidadosamente criadas manualmente. No entanto, é também possível **extrair** ontologias de dados não estruturados, por exemplo, de textos em linguagem natural. -Uma dessas tentativas foi realizada pela Microsoft Research, resultando no [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). +Uma dessas tentativas foi feita pela Microsoft Research, e resultou no [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). -É uma grande coleção de entidades agrupadas usando a relação de herança `is-a`. Permite responder a perguntas como "O que é a Microsoft?" - sendo a resposta algo como "uma empresa com probabilidade de 0,87, e uma marca com probabilidade de 0,75". +É uma grande coleção de entidades agrupadas usando a relação de herança `is-a`. Permite responder a perguntas como "O que é a Microsoft?" – a resposta sendo algo como "uma empresa com probabilidade 0.87, e uma marca com probabilidade 0.75". -O Graph está disponível como API REST ou como um grande ficheiro de texto descarregável que lista todos os pares de entidades. +O Grafo está disponível como REST API, ou como um grande ficheiro de texto descarregável que lista todos os pares de entidades. -## ✍️ Exercício: Um Concept Graph +## ✍️ Exercício: Um Grafo de Conceitos -Experimente o notebook [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) para ver como podemos usar o Microsoft Concept Graph para agrupar artigos de notícias em várias categorias. +Experimente o caderno [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) para ver como podemos usar o Microsoft Concept Graph para agrupar artigos de notícias em várias categorias. ## Conclusão -Hoje em dia, a IA é frequentemente considerada sinónimo de *Machine Learning* ou *Redes Neurais*. No entanto, um ser humano também exibe raciocínio explícito, algo que atualmente não é tratado por redes neurais. Em projetos do mundo real, o raciocínio explícito ainda é usado para realizar tarefas que exigem explicações ou a capacidade de modificar o comportamento do sistema de forma controlada. +Hoje em dia, a IA é frequentemente considerada sinónimo de *Aprendizagem Automática* ou *Redes Neuronais*. No entanto, um ser humano também exibe raciocínio explícito, que é algo que atualmente não é tratado pelas redes neuronais. Em projetos do mundo real, o raciocínio explícito é ainda utilizado para realizar tarefas que requerem explicações, ou a capacidade de modificar o comportamento do sistema de forma controlada. ## 🚀 Desafio -No notebook Family Ontology associado a esta lição, há uma oportunidade de experimentar outras relações familiares. Tente descobrir novas conexões entre pessoas na árvore genealógica. +No caderno Ontologia de Família associado a esta lição, há oportunidade de experimentar outras relações familiares. Tente descobrir novas ligações entre pessoas na árvore genealógica. -## [Questionário pós-aula](https://ff-quizzes.netlify.app/en/ai/quiz/4) +## [Quiz pós-lectura](https://ff-quizzes.netlify.app/en/ai/quiz/4) ## Revisão & Autoestudo -Faça uma pesquisa na internet para descobrir áreas onde os humanos tentaram quantificar e codificar conhecimento. Dê uma olhada na Taxonomia de Bloom e volte na história para aprender como os humanos tentaram entender o mundo. Explore o trabalho de Linnaeus para criar uma taxonomia de organismos e observe como Dmitri Mendeleev criou uma forma de descrever e agrupar elementos químicos. Que outros exemplos interessantes consegue encontrar? +Faça algumas pesquisas na internet para descobrir áreas onde os humanos tentaram quantificar e codificar conhecimento. Consulte a Taxonomia de Bloom, e volte na história para aprender como os humanos tentaram compreender o seu mundo. Explore o trabalho de Linnaeus para criar uma taxonomia de organismos, e observe a forma como Dmitri Mendeleev criou um modo de descrever e agrupar os elementos químicos. Que outros exemplos interessantes pode encontrar? -**Tarefa**: [Construir uma Ontologia](assignment.md) +**Trabalho**: [Construir uma Ontologia](assignment.md) --- + +**Aviso Legal**: +Este documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos empenhemos na precisão, por favor tenha em conta que traduções automatizadas podem conter erros ou imprecisões. O documento original na sua língua nativa deve ser considerado a fonte autorizada. Para informações críticas, recomenda-se a tradução profissional feita por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações erradas decorrentes da utilização desta tradução. + \ No newline at end of file diff --git a/translations/tr/README.md b/translations/tr/README.md index 2fdb8171..1901c9b4 100644 --- a/translations/tr/README.md +++ b/translations/tr/README.md @@ -1,8 +1,8 @@ -[Arapça](../ar/README.md) | [Bengalce](../bn/README.md) | [Bulgarca](../bg/README.md) | [Birmanca (Myanmar)](../my/README.md) | [Çince (Basitleştirilmiş)](../zh/README.md) | [Çince (Geleneksel, Hong Kong)](../hk/README.md) | [Çince (Geleneksel, Makao)](../mo/README.md) | [Çince (Geleneksel, Tayvan)](../tw/README.md) | [Hırvatça](../hr/README.md) | [Çekçe](../cs/README.md) | [Danca](../da/README.md) | [Felemenkçe](../nl/README.md) | [Estonca](../et/README.md) | [Fince](../fi/README.md) | [Fransızca](../fr/README.md) | [Almanca](../de/README.md) | [Yunanca](../el/README.md) | [İbranice](../he/README.md) | [Hintçe](../hi/README.md) | [Macarca](../hu/README.md) | [Endonezce](../id/README.md) | [İtalyanca](../it/README.md) | [Japonca](../ja/README.md) | [Kannada](../kn/README.md) | [Korece](../ko/README.md) | [Litvanca](../lt/README.md) | [Malayca](../ms/README.md) | [Malayalamca](../ml/README.md) | [Marathi](../mr/README.md) | [Nepalce](../ne/README.md) | [Nijerya Pidgincesi](../pcm/README.md) | [Norveççe](../no/README.md) | [Farsça (Persian)](../fa/README.md) | [Lehçe](../pl/README.md) | [Portekizce (Brezilya)](../br/README.md) | [Portekizce (Portekiz)](../pt/README.md) | [Pencapça (Gurmukhi)](../pa/README.md) | [Rumence](../ro/README.md) | [Rusça](../ru/README.md) | [Sırpça (Kirilik)](../sr/README.md) | [Slovakça](../sk/README.md) | [Slovence](../sl/README.md) | [İspanyolca](../es/README.md) | [Svahili](../sw/README.md) | [İsveççe](../sv/README.md) | [Tagalog (Filipince)](../tl/README.md) | [Tamilce](../ta/README.md) | [Telugu](../te/README.md) | [Tayca](../th/README.md) | [Türkçe](./README.md) | [Ukraynaca](../uk/README.md) | [Urduca](../ur/README.md) | [Vietnamca](../vi/README.md) +[Arapça](../ar/README.md) | [Bengalce](../bn/README.md) | [Bulgarca](../bg/README.md) | [Burma (Myanmar)](../my/README.md) | [Çince (Basitleştirilmiş)](../zh/README.md) | [Çince (Geleneksel, Hong Kong)](../hk/README.md) | [Çince (Geleneksel, Makao)](../mo/README.md) | [Çince (Geleneksel, Tayvan)](../tw/README.md) | [Hırvatça](../hr/README.md) | [Çekçe](../cs/README.md) | [Danca](../da/README.md) | [Flemenkçe](../nl/README.md) | [Estonyaca](../et/README.md) | [Fince](../fi/README.md) | [Fransızca](../fr/README.md) | [Almanca](../de/README.md) | [Yunanca](../el/README.md) | [İbranice](../he/README.md) | [Hintçe](../hi/README.md) | [Macarca](../hu/README.md) | [Endonezce](../id/README.md) | [İtalyanca](../it/README.md) | [Japonca](../ja/README.md) | [Kannada](../kn/README.md) | [Korece](../ko/README.md) | [Litvanca](../lt/README.md) | [Malayca](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepalce](../ne/README.md) | [Nijerya Pidgin](../pcm/README.md) | [Norveççe](../no/README.md) | [Farsça (Persian)](../fa/README.md) | [Lehçe](../pl/README.md) | [Portekizce (Brezilya)](../br/README.md) | [Portekizce (Portekiz)](../pt/README.md) | [Pencapça (Gurmukhi)](../pa/README.md) | [Rumence](../ro/README.md) | [Rusça](../ru/README.md) | [Sırpça (Kiril)](../sr/README.md) | [Slovakça](../sk/README.md) | [Slovence](../sl/README.md) | [İspanyolca](../es/README.md) | [Svahili](../sw/README.md) | [İsveççe](../sv/README.md) | [Tagalog (Filipinler)](../tl/README.md) | [Tamilce](../ta/README.md) | [Telugu](../te/README.md) | [Tayca](../th/README.md) | [Türkçe](./README.md) | [Ukraynaca](../uk/README.md) | [Urduca](../ur/README.md) | [Vietnamca](../vi/README.md) -> **Yerel olarak Klonlamayı Tercih Ediyor musunuz?** +> **Yerelde Klonlamayı Tercih Ediyor musunuz?** -> Bu depo 50+ dil çevirisi içerir; bu da indirilen boyutu önemli ölçüde artırır. Çevirileri olmadan klonlamak için sparse checkout kullanabilirsiniz: +> Bu depo, indirme boyutunu önemli ölçüde artıran 50+ dil çevirisi içerir. Çeviriler olmadan klonlamak için spars checkout kullanın: > ```bash > git clone --filter=blob:none --sparse https://github.com/microsoft/AI-For-Beginners.git > cd AI-For-Beginners > git sparse-checkout set --no-cone '/*' '!translations' '!translated_images' > ``` -> Bu size daha hızlı bir indirme ile kursu tamamlamak için gereken her şeyi sağlar. +> Bu size kursu tamamlamak için ihtiyacınız olan her şeyi daha hızlı indirmenizi sağlar. -**Ek dil çevirileri isterseniz, desteklenenler [burada](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md) listelenmiştir** +**Ek dil çeviri desteği için isteklerinizi [buradan](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md) iletebilirsiniz** ## Topluluğa Katılın [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -## Öğrenecekleriniz +## Neler Öğreneceksiniz **[Kursun Zihin Haritası](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** Bu müfredatta öğrenecekleriniz: -* "İyi eski" sembolik yaklaşımı içeren **Bilgi Temsili** ve çıkarım ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)) dahil olmak üzere farklı Yapay Zeka yaklaşımları. -* Modern YZ'nin temelinde yer alan **Sinir Ağları** ve **Derin Öğrenme**. Bu önemli konuların arkasındaki kavramları, en popüler iki framework olan [TensorFlow](http://Tensorflow.org) ve [PyTorch](http://pytorch.org) ile kod örnekleriyle anlatacağız. -* Görüntü ve metinle çalışmak için **Sinirsel Mimariler**. Son modelleri kapsayacağız ancak en son teknoloji biraz eksik olabilir. -* Daha az popüler YZ yaklaşımları, örneğin **Genetik Algoritmalar** ve **Çok Ajanlı Sistemler**. +* Yapay Zekanın farklı yaklaşımları, "iyi eski" sembolik yaklaşım olan **Bilgi Temsili** ve çıkarım ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)) dahil. +* Modern yapay zekanın temelinde olan **Sinir Ağları** ve **Derin Öğrenme**. Bu önemli konuların arkasındaki kavramları, en popüler iki framework olan [TensorFlow](http://Tensorflow.org) ve [PyTorch](http://pytorch.org) içinde kod örnekleriyle göstereceğiz. +* Görüntü ve metinle çalışmak için **Sinir Ağı Mimarileri**. Güncel modelleri kapsayacağız ancak en son durumu tam yansıtmayabilir. +* Daha az popüler AI yaklaşımları, örneğin **Genetik Algoritmalar** ve **Çok Ajanlı Sistemler**. Bu müfredatta kapsamayacağımız konular: -> [Bu kursa ait tüm ek kaynakları Microsoft Learn koleksiyonumuzda bulabilirsiniz](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) +> [Bu kurs için tüm ek kaynakları Microsoft Learn koleksiyonumuzda bulun](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* **İş dünyasında YZ** kullanımının iş vakaları. Microsoft Learn üzerinden [İş kullanıcıları için YZ'ye giriş](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) öğrenme yolunu veya [YZ İş Okulu](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum) programını inceleyebilirsiniz; bu program [INSEAD](https://www.insead.edu/) iş birliği ile geliştirilmiştir. -* Klasik Makine Öğrenimi, bizim [Yeni Başlayanlar İçin Makine Öğrenimi Müfredatımızda](http://github.com/Microsoft/ML-for-Beginners) detaylı olarak anlatılmıştır. -* **[Bilişsel Hizmetler (Cognitive Services)](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)** kullanılarak oluşturulan pratik YZ uygulamaları. Bunun için Microsoft Learn üzerinde [görüntü](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [doğal dil işleme](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[Azure OpenAI Hizmeti ile Üretken YZ](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** gibi modüllerle başlamanızı öneririz. -* Belirli ML **Bulut Frameworkleri**, örneğin [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum), [Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum) veya [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Bu çerçevede [Azure Machine Learning ile makine öğrenimi çözümleri oluşturma ve işletme](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) ve [Azure Databricks ile makine öğrenimi çözümleri oluşturma ve işletme](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum) öğrenme yollarını kullanabilirsiniz. -* **Konuşma Tabanlı YZ** ve **Sohbet Botları**. Bu konuda ayrı bir [Konuşma Tabanlı YZ çözümleri oluşturma](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) öğrenme yolu bulunur; ayrıca daha ayrıntılı bilgi için [bu blog yazısını](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) inceleyebilirsiniz. -* Derin öğrenmenin arkasındaki **Derin Matematik**. Bunun için Ian Goodfellow, Yoshua Bengio ve Aaron Courville tarafından yazılan [Derin Öğrenme](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) kitabını öneririz; kitap çevrimiçi olarak da [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/) adresinde bulunabilir. +* **İş dünyasında AI kullanımı**na dair iş vaka örnekleri. Microsoft Learn’da yer alan [İş kullanıcıları için Yapay Zekaya Giriş](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) öğrenme yolunu veya [AI İş Okulu](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum) programını, [INSEAD](https://www.insead.edu/) iş birliğiyle inceleyebilirsiniz. +* Klasik **Makine Öğrenimi**, [Makine Öğrenimi Yeni Başlayanlar Müfredatımızda](http://github.com/Microsoft/ML-for-Beginners) detaylıca açıklanmıştır. +* **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)** kullanılarak oluşturulan pratik AI uygulamaları. Bunun için Microsoft Learn üzerindeki [görme](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [doğal dil işleme](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[Azure OpenAI Hizmeti ile Üretken AI](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** ve diğer modüllerle başlamanızı öneririz. +* Belirli ML **Bulut Çerçeveleri**, örneğin [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum), [Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum) veya [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). [Azure Machine Learning ile makine öğrenimi çözümleri oluşturma ve işletme](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) ve [Azure Databricks ile makine öğrenimi çözümleri oluşturma ve işletme](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum) öğrenme yollarını değerlendirin. +* **Konuşarak Yapay Zeka** ve **Sohbet Botları**. Ayrı bir [Konuşan yapay zeka çözümleri oluşturma](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) öğrenme yolu bulunmaktadır, ayrıca detaylar için [bu blog yazısını](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) inceleyebilirsiniz. +* Derin öğrenmenin arkasındaki **Derin Matematik**. Bunun için Ian Goodfellow, Yoshua Bengio ve Aaron Courville’nin yazdığı [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) kitabını, ayrıca [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/) üzerinden çevrimiçi olarak öneririz. -_YZ Bulut'taki_ konulara hafif bir giriş için [Azure'da Yapay Zekaya Başlangıç](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum) öğrenme yolunu denenebilirsiniz. +_Bulut’da AI_ konularına yumuşak bir giriş için [Azure üzerinde yapay zekaya başlama](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum) öğrenme yolunu değerlendirebilirsiniz. # İçerik -| | Ders Linki | PyTorch/Keras/TensorFlow | Laboratuvar | +| | Ders Bağlantısı | PyTorch/Keras/TensorFlow | Laboratuvar | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | | 0 | [Kurs Kurulumu](./lessons/0-course-setup/setup.md) | [Geliştirme Ortamınızı Kurun](./lessons/0-course-setup/how-to-run.md) | | -| I | [**YZ'ye Giriş**](./lessons/1-Intro/README.md) | | | -| 01 | [YZ'nin Tanıtımı ve Tarihi](./lessons/1-Intro/README.md) | - | - | -| II | **Sembolik YZ** | +| I | [**Yapay Zekaya Giriş**](./lessons/1-Intro/README.md) | | | +| 01 | [Yapay Zekaya Giriş ve Tarihçe](./lessons/1-Intro/README.md) | - | - | +| II | **Sembolik Yapay Zeka** | | 02 | [Bilgi Temsili ve Uzman Sistemler](./lessons/2-Symbolic/README.md) | [Uzman Sistemler](./lessons/2-Symbolic/Animals.ipynb) / [Ontoloji](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Kavram Grafiği](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | | III | [**Sinir Ağlarına Giriş**](./lessons/3-NeuralNetworks/README.md) ||| -| 03 | [Perceptron](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [Dizüstü](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [Laboratuvar](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | -| 04 | [Çok Katmanlı Perceptron ve Kendi Çerçevemizi Oluşturma](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Dizüstü](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Laboratuvar](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | -| 05 | [Çerçevelere Giriş (PyTorch/TensorFlow) ve Aşırı Öğrenme](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [PyTorch](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [Keras](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Laboratuvar](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | -| IV | [**Bilgisayar Görüşü**](./lessons/4-ComputerVision/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [Microsoft Azure'da Bilgisayar Görüşünü Keşfedin](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | -| 06 | [Bilgisayar Görüşüne Giriş. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Dizüstü](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Laboratuvar](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | -| 07 | [Konvolüsyonel Sinir Ağları](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNN Mimarileri](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [Laboratuvar](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | -| 08 | [Önceden Eğitilmiş Ağlar ve Transfer Öğrenimi](./lessons/4-ComputerVision/08-TransferLearning/README.md) ve [Eğitim Püf Noktaları](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Laboratuvar](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | +| 03 | [Perceptron](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [Notebook](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [Lab](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | +| 04 | [Çok Katmanlı Perceptron ve Kendi Çerçevemizi Oluşturma](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Notebook](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Lab](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | +| 05 | [Çerçevelere Giriş (PyTorch/TensorFlow) ve Aşırı Öğrenme](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [PyTorch](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [Keras](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | +| IV | [**Bilgisayarlı Görüntü İşleme**](./lessons/4-ComputerVision/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [Microsoft Azure'da Bilgisayarlı Görüntüyü Keşfedin](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | +| 06 | [Bilgisayarlı Görüntü İşlemeye Giriş. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Notebook](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Lab](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | +| 07 | [Konvolüsyonel Sinir Ağları](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNN Mimarileri](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [Lab](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | +| 08 | [Önceden Eğitilmiş Ağlar ve Transfer Öğrenme](./lessons/4-ComputerVision/08-TransferLearning/README.md) ve [Eğitim Püf Noktaları](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | | 09 | [Otoenkoderler ve VAE'ler](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | -| 10 | [Üretici Çekişmeli Ağlar ve Sanatsal Stil Transferi](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | -| 11 | [Nesne Tespiti](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [Laboratuvar](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | -| 12 | [Anlamsal Segmentasyon. U-Net](./lessons/4-ComputerVision/12-Segmentation/README.md) | [PyTorch](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb) | | +| 10 | [Üretken Rekabetçi Ağlar & Sanatsal Stil Transferi](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | +| 11 | [Nesne Algılama](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [Lab](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | +| 12 | [Anlamsal Bölütleme. U-Net](./lessons/4-ComputerVision/12-Segmentation/README.md) | [PyTorch](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb) | | | V | [**Doğal Dil İşleme**](./lessons/5-NLP/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [Microsoft Azure'da Doğal Dil İşlemeyi Keşfedin](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| | 13 | [Metin Temsili. Bow/TF-IDF](./lessons/5-NLP/13-TextRep/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb) | | | 14 | [Anlamsal kelime gömme. Word2Vec ve GloVe](./lessons/5-NLP/14-Embeddings/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb) | | -| 15 | [Dil Modelleme. Kendi Gömme Modelinizi Eğitme](./lessons/5-NLP/15-LanguageModeling/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [Laboratuvar](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | +| 15 | [Dil Modelleme. Kendi gömmelerinizi eğitme](./lessons/5-NLP/15-LanguageModeling/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [Lab](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | | 16 | [Tekrarlayan Sinir Ağları](./lessons/5-NLP/16-RNN/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNTF.ipynb) | | -| 17 | [Üretici Tekrarlayan Ağlar](./lessons/5-NLP/17-GenerativeNetworks/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb) | [Laboratuvar](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | -| 18 | [Dönüştürücüler. BERT.](./lessons/5-NLP/18-Transformers/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb) /[TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersTF.ipynb) | | -| 19 | [İsimlendirilmiş Varlık Tanıma](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [Laboratuvar](./lessons/5-NLP/19-NER/lab/README.md) | -| 20 | [Büyük Dil Modelleri, İstek Programlama ve Az Örnekli Görevler](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | +| 17 | [Üretken Tekrarlayan Ağlar](./lessons/5-NLP/17-GenerativeNetworks/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb) | [Lab](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | +| 18 | [Transformers. BERT.](./lessons/5-NLP/18-Transformers/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb) /[TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersTF.ipynb) | | +| 19 | [Adlandırılmış Varlık Tanıma](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [Lab](./lessons/5-NLP/19-NER/lab/README.md) | +| 20 | [Büyük Dil Modelleri, İpucu Programlama ve Az Örnekli Görevler](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | | VI | **Diğer AI Teknikleri** || | -| 21 | [Genetik Algoritmalar](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Dizüstü](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | -| 22 | [Derin Takviyeli Öğrenme](./lessons/6-Other/22-DeepRL/README.md) | [PyTorch](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[TensorFlow](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [Laboratuvar](./lessons/6-Other/22-DeepRL/lab/README.md) | -| 23 | [Çok Ajanlı Sistemler](./lessons/6-Other/23-MultiagentSystems/README.md) | | | +| 21 | [Genetik Algoritmalar](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Notebook](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | +| 22 | [Derin Takviyeli Öğrenme](./lessons/6-Other/22-DeepRL/README.md) | [PyTorch](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[TensorFlow](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [Lab](./lessons/6-Other/22-DeepRL/lab/README.md) | +| 23 | [Çoklu Ajan Sistemleri](./lessons/6-Other/23-MultiagentSystems/README.md) | | | | VII | **AI Etiği** | | | | 24 | [AI Etiği ve Sorumlu AI](./lessons/7-Ethics/README.md) | [Microsoft Learn: Sorumlu AI İlkeleri](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | -| IX | **Ekstra İçerikler** | | | -| 25 | [Çok Modlu Ağlar, CLIP ve VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Dizüstü](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | +| IX | **Ekstralar** | | | +| 25 | [Çok Modlu Ağlar, CLIP ve VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Notebook](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | -## Her ders içeriği +## Her ders şunları içerir -* Ön okuma materyali -* Çoğunlukla belirli bir çerçeveye özgü (**PyTorch** veya **TensorFlow**) çalıştırılabilir Jupyter Dizüstüleri. Çalıştırılabilir dizüstü aynı zamanda çok sayıda teorik materyal içerir, bu yüzden konuyu anlamak için dizüstünün en az bir sürümünü (PyTorch veya TensorFlow) incelemeniz gerekmektedir. -* Bazı konular için mevcut olan **Laboratuvarlar**, öğrendiğiniz materyali belirli bir probleme uygulama şansı verir. -* Bazı bölümlerde ilgili konuları kapsayan [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) modüllerine linkler bulunur. +* Ön okuma materyali +* Çoğunlukla çerçeveye özgü (**PyTorch** veya **TensorFlow**) çalıştırılabilir Jupyter Notebook’lar. Çalıştırılabilir notebook ayrıca çok fazla teorik materyal içerir; bu nedenle konuyu anlamak için en az bir versiyondan (PyTorch veya TensorFlow) geçmeniz gerekir. +* Bazı konular için, öğrendiğiniz materyali belirli bir probleme uygulama fırsatı veren **laboratuvarlar**. +* Bazı bölümler, ilgili konuları kapsayan [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) modüllerine bağlantılar içerir. ## Başlarken -### 🎯 AI'ya Yeni misiniz? Buradan Başlayın! +### 🎯 AI’ya yeni misiniz? Buradan başlayın! -AI'ya tamamen yeniyseniz ve hızlı, uygulamalı örnekler arıyorsanız, [**Yeni Başlayanlar İçin Dostça Örnekler**](./examples/README.md) bölümüne bakın! Bunlar arasında: +Eğer yapay zekaya tamamen yeniseniz ve hızlı, uygulamalı örnekler arıyorsanız, [**Yeni Başlayanlar için Dostça Örnekler**](./examples/README.md) sayfamıza göz atın! Bunlar şunları içerir: -- 🌟 **Merhaba AI Dünyası** - İlk AI programınız (desen tanıma) -- 🧠 **Basit Sinir Ağı** - Baştan bir sinir ağı inşa edin -- 🖼️ **Görüntü Sınıflandırıcı** - Detaylı yorumlarla görüntü sınıflandırma -- 💬 **Metin Duyarlılığı** - Pozitif/negatif metni analiz edin +- 🌟 **Merhaba AI Dünyası** - İlk AI programınız (desen tanıma) +- 🧠 **Basit Sinir Ağı** - Kendi sinir ağınızı baştan inşa edin +- 🖼️ **Görüntü Sınıflandırıcı** - Detaylı yorumlarla görüntüleri sınıflandırın +- 💬 **Metin Duygu Analizi** - Pozitif/negatif metni analiz etme -Bu örnekler, tam müfredata dalmadan önce yapay zeka kavramlarını anlamanıza yardımcı olmak için tasarlanmıştır. +Bu örnekler, tam müfredata geçmeden önce AI kavramlarını anlamanıza yardımcı olmak için tasarlanmıştır. ### 📚 Tam Müfredat Kurulumu -- Geliştirme ortamınızı kurmanıza yardımcı olmak için bir [kurulum dersi](./lessons/0-course-setup/setup.md) oluşturduk. - Eğitimciler için de bir [müfredat kurulum dersi](./lessons/0-course-setup/for-teachers.md) oluşturduk! -- [Kodun VSCode veya Codepace'te nasıl çalıştırılacağı](./lessons/0-course-setup/how-to-run.md) +- Geliştirme ortamınızı kurmanıza yardımcı olmak için bir [kurulum dersi](./lessons/0-course-setup/setup.md) oluşturduk. - Eğitmenler için de bir [müfredat kurulum dersi](./lessons/0-course-setup/for-teachers.md) hazırladık! +- Kodu [VSCode veya Codespace'de nasıl çalıştıracağınızı](./lessons/0-course-setup/how-to-run.md) öğrenin -Bu adımları izleyin: +Aşağıdaki adımları takip edin: -Depoyu Çatallayın: Bu sayfanın sağ üst köşesindeki "Fork" düğmesine tıklayın. +Depoyu çatallayın: Bu sayfanın sağ üst köşesindeki "Fork" düğmesine tıklayın. -Depoyu Klonlayın: `git clone https://github.com/microsoft/AI-For-Beginners.git` +Depoyu klonlayın: `git clone https://github.com/microsoft/AI-For-Beginners.git` -Daha sonra bulmayı kolaylaştırmak için bu depoya yıldız (🌟) vermeyi unutmayın. +Daha sonra daha kolay bulabilmek için bu depoya yıldız (🌟) vermeyi unutmayın. -## Diğer Öğrenicilerle Tanışın +## Diğer Öğrenenlerle Tanışın -Bu kursu alan diğer öğrenicilerle tanışmak ve ağ kurmak, destek almak için [resmi AI Discord sunucumuza](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) katılın. +Bu kursu alan diğer öğrenenlerle tanışmak ve iletişim kurmak, destek almak için [resmi AI Discord sunucumuza](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) katılın. -Ürün geri bildiriminiz veya sorularınız varsa, inşa ederken [Azure AI Foundry Geliştirici Forumumuzu](https://aka.ms/foundry/forum) ziyaret edin. +Ürün geri bildiriminiz veya sorularınız varsa, inşa ederken [Azure AI Foundry Geliştirici Forumu](https://aka.ms/foundry/forum)'nu ziyaret edin. -## Quizler +## Quizler -> **Quizler hakkında bir not**: Tüm quizler etc\quiz-app içindeki Quiz-app klasöründe veya [Online olarak burada](https://ff-quizzes.netlify.app/) bulunmaktadır. Derslerin içinden bağlantılıdır. Quiz uygulaması yerel olarak çalıştırılabilir veya Azure'a dağıtılabilir; `quiz-app` klasöründeki talimatları izleyin. Kademeli olarak yerelleştirilmektedir. +> **Quizlerle ilgili bir not**: Tüm quizler Quiz-app klasörü içinde etc\quiz-app dizininde ya da [Çevrimiçi Burada](https://ff-quizzes.netlify.app/) bulunur. Quizler dersler içinde bağlantılıdır, quiz uygulaması yerel olarak çalıştırılabilir veya Azure’a dağıtılabilir; `quiz-app` klasöründeki talimatları takip edin. Quizler kademeli olarak yerelleştirilmektedir. -## Yardım İstiyoruz +## Yardım İsteniyor -Önerileriniz veya yazım ya da kod hataları bulduysanız sorun açın veya bir pull request oluşturun. +Önerileriniz veya yazım ya da kod hataları bulduysanız, bir issue açın veya bir pull request oluşturun. -## Özel Teşekkürler +## Özel Teşekkür -* **✍️ Birincil Yazar:** [Dmitry Soshnikov](http://soshnikov.com), PhD -* **🔥 Editör:** [Jen Looper](https://twitter.com/jenlooper), PhD -* **🎨 Sketchnote çizeri:** [Tomomi Imura](https://twitter.com/girlie_mac) -* **✅ Quiz Oluşturucu:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) -* **🙏 Temel Katkıda Bulunanlar:** [Evgenii Pishchik](https://github.com/Pe4enIks) +* **✍️ Birincil Yazar:** [Dmitry Soshnikov](http://soshnikov.com), PhD +* **🔥 Editör:** [Jen Looper](https://twitter.com/jenlooper), PhD +* **🎨 Sketchnote illüstratörü:** [Tomomi Imura](https://twitter.com/girlie_mac) +* **✅ Quiz Oluşturucu:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) +* **🙏 Ana Katkıda Bulunanlar:** [Evgenii Pishchik](https://github.com/Pe4enIks) ## Diğer Müfredatlar -Ekibimiz başka müfredatlar üretiyor! Şunlara göz atın: +Ekibimiz diğer müfredatlar da hazırlıyor! Göz atın: ### LangChain @@ -197,30 +198,30 @@ Ekibimiz başka müfredatlar üretiyor! Şunlara göz atın: --- -### Temel Öğrenme +### Temel Öğrenim [![ML for Beginners](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) [![Data Science for Beginners](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst) [![AI for Beginners](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst) -[![Cybersecurity for Beginners](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung) -[![Web Dev for Beginners](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst) -[![IoT for Beginners](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst) -[![XR Development for Beginners](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst) +[![Siber Güvenlik Başlangıç](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung) +[![Başlangıç için Web Geliştirme](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst) +[![Başlangıç için IoT](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst) +[![Başlangıç için XR Geliştirme](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst) --- ### Copilot Serisi -[![Copilot for AI Paired Programming](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) -[![Copilot for C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst) -[![Copilot Adventure](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst) +[![AI Eşliğinde Programlama için Copilot](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) +[![C#/.NET için Copilot](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst) +[![Copilot Macerası](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst) ## Yardım Alma -AI uygulamaları geliştirme ile ilgili takılır veya sorunuz olursa, MCP hakkında tartışmalara katılmak için diğer öğreniciler ve deneyimli geliştiricilerle birleşin. Soruların hoş karşılandığı ve bilginin özgürce paylaşıldığı destekleyici bir topluluktur. +Takıldığınızda ya da AI uygulamaları geliştirme ile ilgili sorularınız olduğunda, MCP hakkında diğer öğrenenler ve deneyimli geliştiricilerle tartışmalara katılın. Soruların memnuniyetle karşılandığı ve bilginin özgürce paylaşıldığı destekleyici bir topluluktur. [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -Ürün geri bildiriminiz veya oluşturma sırasında hatalarınız varsa: +Ürün geri bildiriminiz veya inşaat sırasında hatalar varsa şurayı ziyaret edin: [![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) @@ -228,5 +229,5 @@ AI uygulamaları geliştirme ile ilgili takılır veya sorunuz olursa, MCP hakk **Feragatname**: -Bu belge, yapay zeka çeviri servisi [Co-op Translator](https://github.com/Azure/co-op-translator) kullanılarak çevrilmiştir. Doğruluk için çaba göstermekle birlikte, otomatik çevirilerin hata veya yanlışlık içerebileceğini lütfen unutmayınız. Orijinal belge, kendi ana dilindeki metin otoritatif kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımıyla ortaya çıkabilecek herhangi bir yanlış anlama veya yorumdan dolayı sorumluluk kabul edilmemektedir. +Bu belge, AI çeviri hizmeti [Co-op Translator](https://github.com/Azure/co-op-translator) kullanılarak çevrilmiştir. Doğruluk için çaba göstermemize rağmen, otomatik çevirilerin hatalar veya yanlışlıklar içerebileceğini lütfen unutmayınız. Orijinal belge, asıl dilinde yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımı sonucunda oluşabilecek herhangi bir yanlış anlama veya yorum hatasından sorumlu değiliz. \ No newline at end of file diff --git a/translations/tr/lessons/0-course-setup/how-to-run.md b/translations/tr/lessons/0-course-setup/how-to-run.md index 24da54fa..8855ffa3 100644 --- a/translations/tr/lessons/0-course-setup/how-to-run.md +++ b/translations/tr/lessons/0-course-setup/how-to-run.md @@ -1,21 +1,21 @@ -# Kodu Çalıştırma Yöntemleri +# Kodu Çalıştırma -Bu müfredat, çalıştırılabilir birçok örnek ve laboratuvar içermektedir ve bunları çalıştırmak isteyeceksiniz. Bunu yapmak için, bu müfredatın bir parçası olarak sağlanan Jupyter Notebooks'ta Python kodu çalıştırma yeteneğine ihtiyacınız var. Kodu çalıştırmak için birkaç seçeneğiniz var: +Bu müfredat, çalıştırmak isteyebileceğiniz birçok yürütülebilir örnek ve laboratuvar içerir. Bunu yapmak için, bu müfredatın parçası olarak sağlanan Jupyter Not defterlerinde Python kodu çalıştırma yeteneğine ihtiyacınız vardır. Kodu çalıştırmak için birkaç seçeneğiniz vardır: -## Kendi Bilgisayarınızda Yerel Olarak Çalıştırma +## Bilgisayarınızda Yerel Olarak Çalıştırma -Kodu kendi bilgisayarınızda yerel olarak çalıştırmak için bir Python sürümünün yüklü olması gerekir. Şahsen, **[miniconda](https://conda.io/en/latest/miniconda.html)** yüklemenizi öneririm - bu, farklı Python **sanal ortamlarını** destekleyen `conda` paket yöneticisini içeren oldukça hafif bir kurulumdur. +Kodu bilgisayarınızda yerel olarak çalıştırmak için Python kurulumu gereklidir. Tavsiye edilenlerden biri **[miniconda](https://conda.io/en/latest/miniconda.html)** yüklemektir - bu, farklı Python **sanal ortamları** için `conda` paket yöneticisini destekleyen oldukça hafif bir kurulumdur. -Miniconda'yı yükledikten sonra, bu kurs için kullanılacak bir sanal ortam oluşturmak ve depoyu klonlamak için şu adımları izleyin: +Miniconda'yı yükledikten sonra, depoyu klonlayın ve bu ders için kullanılacak bir sanal ortam oluşturun: ```bash git clone http://github.com/microsoft/ai-for-beginners @@ -24,53 +24,57 @@ conda env create --name ai4beg --file .devcontainer/environment.yml conda activate ai4beg ``` -### Python Uzantısı ile Visual Studio Code Kullanımı +### Visual Studio Code ile Python Eklentisi Kullanımı -Muhtemelen müfredatı kullanmanın en iyi yolu, [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) ile [Python Uzantısı](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste) kullanarak açmaktır. +Bu müfredat, en iyi şekilde [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) içindeki [Python Eklentisi](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste) ile açıldığında kullanılır. -> **Not**: Depoyu klonlayıp VS Code'da açtığınızda, Python uzantılarını yüklemenizi otomatik olarak önerecektir. Yukarıda açıklandığı gibi miniconda'yı da yüklemeniz gerekecektir. +> **Not**: Depoyu klonlayıp VS Code’da dizini açtıktan sonra, Python eklentilerini yüklemenizi otomatik olarak önerecektir. Ayrıca yukarıda anlatıldığı gibi miniconda'yı da yüklemeniz gerekecektir. -> **Not**: VS Code, depoyu bir konteyner içinde yeniden açmanızı önerirse, yerel Python kurulumunu kullanmak için bunu reddetmeniz gerekir. +> **Not**: VS Code, depoyu bir konteyner içinde yeniden açmanızı önerirse, yerel Python kurulumunu kullanmak için bunu reddetmelisiniz. ### Tarayıcıda Jupyter Kullanımı -Jupyter ortamını kendi bilgisayarınızda tarayıcı üzerinden de kullanabilirsiniz. Aslında, hem klasik Jupyter hem de Jupyter Hub, otomatik tamamlama, kod vurgulama vb. özelliklerle oldukça kullanışlı bir geliştirme ortamı sunar. +Ayrıca kendi bilgisayarınızda tarayıcıdan Jupyter ortamı kullanabilirsiniz. Hem klasik Jupyter hem de JupyterHub, otomatik tamamlama, kod vurgulama gibi özelliklerle kullanışlı bir geliştirme ortamı sağlar. -Jupyter'i yerel olarak başlatmak için, kursun dizinine gidin ve şu komutları çalıştırın: +Jupyter'ı yerel olarak başlatmak için ders dizinine gidin ve şunu yürütün: ```bash jupyter notebook -``` -veya +``` + veya ```bash jupyterhub -``` -Daha sonra herhangi bir `.ipynb` dosyasına gidip, açabilir ve çalışmaya başlayabilirsiniz. +``` + Daha sonra herhangi bir `.ipynb` dosyasına gidip açabilir ve çalışmaya başlayabilirsiniz. ### Konteynerde Çalıştırma -Python kurulumuna bir alternatif, kodu bir konteyner içinde çalıştırmaktır. Depomuz, bu depo için bir konteynerin nasıl oluşturulacağını açıklayan özel bir `.devcontainer` klasörü içerdiğinden, VS Code size kodu bir konteyner içinde yeniden açmayı teklif edecektir. Bu, Docker kurulumunu gerektirir ve daha karmaşık olabilir, bu nedenle bunu daha deneyimli kullanıcılara öneriyoruz. +Python kurulumu yapmak yerine, kodu bir konteynerde çalıştırmak bir alternatiftir. Depomuz, bu depo için nasıl bir konteyner oluşturulacağını gösteren özel bir `.devcontainer` klasörü sağlar ve VS Code, kodu bir konteynerde yeniden açma fırsatı sunar. Bunun için Docker kurulumu gerekir ve biraz daha karmaşıktır, bu yüzden daha deneyimli kullanıcılara tavsiye edilir. ## Bulutta Çalıştırma -Python'u yerel olarak kurmak istemiyorsanız ve bazı bulut kaynaklarına erişiminiz varsa, kodu bulutta çalıştırmak iyi bir alternatif olabilir. Bunu yapmanın birkaç yolu vardır: +Python'u yerel olarak kurmak istemiyorsanız ve bazı bulut kaynaklarına erişiminiz varsa - kodu bulutta çalıştırmak iyi bir alternatif olabilir. Bunu yapmanın birkaç yolu vardır: -* **[GitHub Codespaces](https://github.com/features/codespaces)** kullanarak. Bu, GitHub'da sizin için oluşturulan ve VS Code tarayıcı arayüzü üzerinden erişilebilen bir sanal ortamdır. Codespaces erişiminiz varsa, depodaki **Code** düğmesine tıklayıp bir codespace başlatabilir ve hemen çalışmaya başlayabilirsiniz. -* **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)** kullanarak. [Binder](https://mybinder.org), GitHub'daki bazı kodları test etmek isteyen kişiler için bulutta ücretsiz hesaplama kaynakları sağlar. Ana sayfada depoyu Binder'da açmak için bir düğme vardır - bu, sizi hızla Binder sitesine yönlendirecek, temel konteyneri oluşturacak ve Jupyter web arayüzünü sorunsuz bir şekilde başlatacaktır. +* **[GitHub Codespaces](https://github.com/features/codespaces)** kullanmak, GitHub üzerinde sizin için oluşturulan, VS Code tarayıcı arayüzü ile erişilebilen sanal bir ortamdır. Eğer Codespaces erişiminiz varsa, depoda **Code** düğmesine tıklayabilir, bir codespace başlatabilir ve kısa sürede çalışmaya başlayabilirsiniz. +* **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)** kullanmak. [Binder](https://mybinder.org), GitHub’daki bazı kodları denemeniz için bulutta ücretsiz hesaplama kaynakları sunar. Ana sayfada depoyu Binder'da açmak için bir düğme vardır - bu sizi hızla binder sitesine götürür, alt yapısında bir konteyner oluşturur ve sorunsuzca Jupyter web arayüzünü başlatır. -> **Not**: Kötüye kullanımı önlemek için, Binder bazı web kaynaklarına erişimi engellemiştir. Bu, modelleri ve/veya veri setlerini genel internetten çeken bazı kodların çalışmasını engelleyebilir. Bazı alternatif çözümler bulmanız gerekebilir. Ayrıca, Binder tarafından sağlanan hesaplama kaynakları oldukça temel düzeydedir, bu nedenle özellikle daha karmaşık derslerde eğitim yavaş olacaktır. +> **Not**: Kötüye kullanımı önlemek için, Binder bazı web kaynaklarına erişimi engellemiştir. Bu, modelleri ve/veya veri setlerini genel İnternetten çeken bazı kodların çalışmasını engelleyebilir. Bazı çözümler bulmanız gerekebilir. Ayrıca, Binder tarafından sağlanan hesaplama kaynakları oldukça temel seviyededir, bu nedenle eğitim özellikle daha karmaşık sonraki derslerde yavaş olacaktır. ## GPU ile Bulutta Çalıştırma -Bu müfredattaki bazı ileri düzey dersler, GPU desteğinden büyük ölçüde faydalanacaktır, çünkü aksi takdirde eğitim süreci oldukça yavaş olacaktır. Özellikle [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) veya kurumunuz aracılığıyla buluta erişiminiz varsa, aşağıdaki seçenekleri değerlendirebilirsiniz: +Bu müfredattaki bazı ileri dersler GPU desteğinden çok fayda sağlar. Örneğin model eğitimi aksi halde çok yavaş olabilir. Özellikle [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) veya kurumunuz aracılığıyla buluta erişiminiz varsa, takip edebileceğiniz birkaç seçenek vardır: -* [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) oluşturun ve Jupyter üzerinden bağlanın. Daha sonra depoyu doğrudan makineye klonlayabilir ve öğrenmeye başlayabilirsiniz. NC serisi sanal makineler GPU desteğine sahiptir. +* [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) oluşturun ve Jupyter üzerinden bağlanın. Depoyu doğrudan bu makineye klonlayabilir ve öğrenmeye başlayabilirsiniz. NC-serisi VM’ler GPU desteğine sahiptir. -> **Not**: Azure for Students dahil bazı abonelikler, GPU desteğini varsayılan olarak sağlamaz. Teknik destek talebiyle ek GPU çekirdekleri talep etmeniz gerekebilir. +> **Not**: Azure for Students dahil bazı abonelikler kutudan çıktığı gibi GPU desteği sağlamaz. Ek GPU çekirdeği için teknik destek talebi yapmanız gerekebilir. -* [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) oluşturun ve oradaki Notebook özelliğini kullanın. [Bu video](https://azure-for-academics.github.io/quickstart/azureml-papers/), bir depoyu Azure ML not defterine nasıl klonlayacağınızı ve kullanmaya başlayacağınızı gösterir. +* [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) oluşturun ve oradaki Notebook özelliğini kullanın. [Bu video](https://azure-for-academics.github.io/quickstart/azureml-papers/) bir depoyu Azure ML dizinine nasıl klonlayacağınızı ve kullanmaya başlayacağınızı gösterir. -Ayrıca, ücretsiz GPU desteği sunan Google Colab'i kullanabilir ve Jupyter Notebooks'u oraya yükleyerek tek tek çalıştırabilirsiniz. +Ayrıca Google Colab kullanabilirsiniz; ücretsiz GPU desteğiyle gelir ve Jupyter Not defterlerini oraya yükleyip tek tek çalıştırabilirsiniz. +--- + + **Feragatname**: -Bu belge, [Co-op Translator](https://github.com/Azure/co-op-translator) adlı yapay zeka çeviri hizmeti kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan herhangi bir yanlış anlama veya yanlış yorumlama durumunda sorumluluk kabul edilmez. \ No newline at end of file +Bu belge, AI çeviri hizmeti [Co-op Translator](https://github.com/Azure/co-op-translator) kullanılarak çevrilmiştir. Doğruluk için çaba gösterilse de, otomatik çevirilerin hatalar veya yanlışlıklar içerebileceğini lütfen unutmayınız. Orijinal belge, kendi ana dilinde yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çeviri kullanımı sonucunda oluşabilecek herhangi bir yanlış anlama veya yorumlama için sorumluluk kabul edilmemektedir. + \ No newline at end of file diff --git a/translations/tr/lessons/2-Symbolic/Animals.ipynb b/translations/tr/lessons/2-Symbolic/Animals.ipynb index 09836686..3d851483 100644 --- a/translations/tr/lessons/2-Symbolic/Animals.ipynb +++ b/translations/tr/lessons/2-Symbolic/Animals.ipynb @@ -6,25 +6,25 @@ "collapsed": true }, "source": [ - "# Hayvan Uzman Sistemi Uygulaması\n", + "# Bir Hayvan Uzman Sistemi Uygulamak\n", "\n", - "[AI for Beginners Müfredatı](http://github.com/microsoft/ai-for-beginners) örneğinden bir alıntı.\n", + "[AI for Beginners Curriculum](http://github.com/microsoft/ai-for-beginners) örneğinden.\n", "\n", - "Bu örnekte, bazı fiziksel özelliklere dayanarak bir hayvanı belirlemek için basit bir bilgi tabanlı sistem uygulayacağız. Sistem aşağıdaki AND-OR ağacı ile temsil edilebilir (bu, ağacın bir kısmıdır, kolayca daha fazla kural ekleyebiliriz):\n", + "Bu örnekte, bazı fiziksel özelliklere dayanarak bir hayvanı belirlemek için basit bir bilgi tabanlı sistem uygulayacağız. Sistem, aşağıdaki VE-VEYA ağacı ile temsil edilebilir (bu, tüm ağacın bir parçasıdır, kolayca bazı ek kurallar ekleyebiliriz):\n", "\n", - "![](../../../../translated_images/tr/AND-OR-Tree.5592d2c70187f283.webp)\n" + "![](../../../../../../translated_images/tr/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Kendi uzman sistemleri kabuğumuz ile geri yönlü çıkarım\n", + "## Geriye dönük çıkarımlı kendi uzman sistem kabuğumuz\n", "\n", - "Üretim kurallarına dayalı bilgi temsili için basit bir dil tanımlamayı deneyelim. Kuralları tanımlamak için Python sınıflarını anahtar kelimeler olarak kullanacağız. Temelde 3 tür sınıf olacaktır:\n", - "* `Ask`, kullanıcıya sorulması gereken bir soruyu temsil eder. Olası cevapların kümesini içerir.\n", - "* `If`, bir kuralı temsil eder ve sadece kuralın içeriğini saklamak için kullanılan bir sözdizimi şeklidir.\n", - "* `AND`/`OR`, ağacın AND/OR dallarını temsil eden sınıflardır. Sadece içindeki argümanların listesini saklarlar. Kodun basitleştirilmesi için tüm işlevsellik üst sınıf olan `Content` içinde tanımlanmıştır.\n" + "Üretim kurallarına dayalı bilgi temsil için basit bir dil tanımlamaya çalışalım. Kuralları tanımlamak için anahtar kelimeler olarak Python sınıflarını kullanacağız. Temelde 3 tür sınıf olacaktır:\n", + "* `Ask`, kullanıcıya sorulması gereken bir soruyu temsil eder. Olası cevaplar kümesini içerir.\n", + "* `If`, bir kuralı temsil eder ve kuralın içeriğini saklamak için sadece sentaktik şeker gibidir.\n", + "* `AND`/`OR`, ağacın AND/OR dallarını temsil eden sınıflardır. İçerideki argüman listesini saklarlar. Kodu basitleştirmek için, tüm işlevsellik üst sınıf `Content` içinde tanımlanmıştır.\n" ] }, { @@ -66,7 +66,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Sistemimizde, çalışma belleği **özellik-değer çiftleri** olarak **bilgiler** listesini içerecektir. Bilgi tabanı, eylemleri (çalışma belleğine eklenmesi gereken yeni bilgileri) AND-OR ifadeleri olarak ifade edilen koşullara eşleyen büyük bir sözlük olarak tanımlanabilir. Ayrıca, bazı bilgiler `Sor`ulabilir.\n" + "Sistemimizde, çalışma belleği **özellik-değer çiftleri** olarak **gerçekler** listesini içerir. Bilgi tabanı, eylemleri (çalışma belleğine eklenmesi gereken yeni gerçekler) AND-OR ifadeleriyle ifade edilen koşullara eşleyen büyük bir sözlük olarak tanımlanabilir. Ayrıca, bazı gerçekler `Ask` ile sorulabilir.\n" ] }, { @@ -99,13 +99,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Geriye dönük çıkarım yapmak için `Knowledgebase` sınıfını tanımlayacağız. Bu sınıf şunları içerecek:\n", - "* Çalışma `hafızası` - nitelikleri değerlere eşleyen bir sözlük\n", - "* Yukarıda tanımlandığı formatta Bilgi tabanı `kuralları`\n", + "Geriye doğru çıkarım yapmak için, `Knowledgebase` sınıfını tanımlayacağız. İçerecek:\n", + "* Çalışan `memory` - nitelikleri değerlere eşleyen bir sözlük\n", + "* Yukarıda tanımlandığı biçimdeki Bilgi tabanı `rules`\n", "\n", "İki ana yöntem şunlardır:\n", - "* Gerekirse çıkarım yaparak bir niteliğin değerini elde etmek için kullanılan `get`. Örneğin, `get('color')` bir renk slotunun değerini alır (gerekirse sorar ve çalışma hafızasında daha sonra kullanmak üzere değeri saklar). Eğer `get('color:blue')` sorulursa, bir renk sorar ve ardından renge bağlı olarak `y`/`n` değerini döndürür.\n", - "* `eval`, gerçek çıkarımı gerçekleştirir, yani AND/OR ağacını dolaşır, alt hedefleri değerlendirir, vb.\n" + "* Gerekirse çıkarım yaparak bir niteliğin değerini elde etmek için `get`. Örneğin, `get('color')` bir renk yuvasının değerini alır (gerekirse sorar ve bu değeri daha sonra kullanım için çalışan belleğe kaydeder). `get('color:blue')` derseniz, renk sorar ve ardından renge bağlı olarak `y`/`n` değerini döndürür.\n", + "* `eval` gerçek çıkarımı gerçekleştirir, yani VE/VEYA ağacında gezinir, alt hedefleri değerlendirir vb.\n" ] }, { @@ -172,7 +172,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Şimdi hayvan bilgi tabanımızı tanımlayalım ve danışmayı gerçekleştirelim. Bu çağrının size sorular soracağını unutmayın. Evet-hayır soruları için `e`/`h` yazarak veya daha uzun çoktan seçmeli sorular için sayı (0..N) belirterek cevap verebilirsiniz.\n" + "Şimdi hayvan bilgi tabanımızı tanımlayalım ve danışmanlığı gerçekleştirelim. Unutmayın, bu çağrı size sorular soracak. Evet-hayır sorularına `y`/`n` yazarak, daha uzun çoktan seçmeli sorulara ise 0..N arasında bir sayı belirterek cevap verebilirsiniz.\n" ] }, { @@ -229,11 +229,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## PyKnow ile İleri Çıkarım Kullanımı\n", + "## İleri Çıkarım için Experta Kullanımı\n", "\n", - "Bir sonraki örnekte, bilgi temsili için kullanılan kütüphanelerden biri olan [PyKnow](https://github.com/buguroo/pyknow/) ile ileri çıkarım uygulamaya çalışacağız. **PyKnow**, Python'da ileri çıkarım sistemleri oluşturmak için kullanılan bir kütüphanedir ve klasik eski sistem [CLIPS](http://www.clipsrules.net/index.html)'e benzer şekilde tasarlanmıştır.\n", + "Bir sonraki örnekte, bilgi temsili için kullanılan kütüphanelerden biri olan [Experta](https://github.com/nilp0inter/experta) kullanarak ileri çıkarımı uygulamaya çalışacağız. **Experta**, Python'da ileri çıkarım sistemleri oluşturmak için geliştirilmiş bir kütüphanedir ve klasik eski sistem [CLIPS](http://www.clipsrules.net/index.html) ile benzer olacak şekilde tasarlanmıştır.\n", "\n", - "İleri zincirleme işlemini kendimiz de kolaylıkla uygulayabilirdik, ancak basit uygulamalar genellikle çok verimli değildir. Daha etkili kural eşleştirme için özel bir algoritma olan [Rete](https://en.wikipedia.org/wiki/Rete_algorithm) kullanılır.\n" + "İleri zincirlemeyi kendimiz de çok sorun yaşamadan uygulayabilirdik, ancak sezgisel (naif) uygulamalar genellikle çok verimli olmaz. Daha etkili kural eşleştirme için özel bir algoritma [Rete](https://en.wikipedia.org/wiki/Rete_algorithm) kullanılır.\n" ] }, { @@ -247,32 +247,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "Collecting git+https://github.com/buguroo/pyknow/\n", - " Cloning https://github.com/buguroo/pyknow/ to /tmp/pip-req-build-3cqeulyl\n", - " Running command git clone --filter=blob:none --quiet https://github.com/buguroo/pyknow/ /tmp/pip-req-build-3cqeulyl\n", - " Resolved https://github.com/buguroo/pyknow/ to commit 48818336f2e9a126f1964f2d8dc22d37ff800fe8\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting frozendict==1.2\n", - " Using cached frozendict-1.2.tar.gz (2.6 kB)\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting schema==0.6.7\n", - " Using cached schema-0.6.7-py2.py3-none-any.whl (14 kB)\n", - "Building wheels for collected packages: pyknow, frozendict\n", - " Building wheel for pyknow (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for pyknow: filename=pyknow-1.7.0-py3-none-any.whl size=34228 sha256=b7de5b09292c4007667c72f69b98d5a1b5f7324ff15f9dd8e077c3d5f7aade42\n", - " Stored in directory: /tmp/pip-ephem-wheel-cache-k7jpave7/wheels/81/1a/d3/f6c15dbe1955598a37755215f2a10449e7418500d7bd4b9508\n", - " Building wheel for frozendict (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for frozendict: filename=frozendict-1.2-py3-none-any.whl size=3148 sha256=2863d55c240d2409cddf05ccfe600591f8478681549fc97555c47c90dc6bb160\n", - " Stored in directory: /home/rg/.cache/pip/wheels/49/ac/f8/cb8120244e710bdb479c86198b03c7b08c3c2d3d2bf448fd6e\n", - "Successfully built pyknow frozendict\n", - "Installing collected packages: schema, frozendict, pyknow\n", - "Successfully installed frozendict-1.2 pyknow-1.7.0 schema-0.6.7\n" + "Collecting git+https://github.com/nilp0inter/experta\n", + " Cloning https://github.com/nilp0inter/experta to /tmp/pip-req-build-7qurtwk3\n", + " Running command git clone --filter=blob:none --quiet https://github.com/nilp0inter/experta /tmp/pip-req-build-7qurtwk3\n", + " Resolved https://github.com/nilp0inter/experta to commit c6d5834b123861f5ae09e7d07027dc98bec58741\n", + " Installing build dependencies ... \u001b[?25ldone\n", + "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", + "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25hRequirement already satisfied: frozendict~=2.4.6 in /opt/conda/envs/ai4beg/lib/python3.12/site-packages (from experta==1.9.5.dev1) (2.4.7)\n", + "Collecting schema~=0.6.7 (from experta==1.9.5.dev1)\n", + " Downloading schema-0.6.8-py2.py3-none-any.whl.metadata (14 kB)\n", + "Downloading schema-0.6.8-py2.py3-none-any.whl (14 kB)\n", + "Building wheels for collected packages: experta\n", + " Building wheel for experta (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25h Created wheel for experta: filename=experta-1.9.5.dev1-py3-none-any.whl size=34804 sha256=888c459512a5e713f4b674caa9a0f96cfdf07ec0d6eb56cc318ce0653d218014\n", + " Stored in directory: /tmp/pip-ephem-wheel-cache-1eeii9zy/wheels/3d/e8/bb/22d7956359603fa8dd679aa09f5b8efb3f29991c3986fdc787\n", + "Successfully built experta\n", + "Installing collected packages: schema, experta\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2/2\u001b[0m [experta]\n", + "\u001b[1A\u001b[2KSuccessfully installed experta-1.9.5.dev1 schema-0.6.8\n" ] } ], "source": [ "import sys\n", - "!{sys.executable} -m pip install git+https://github.com/buguroo/pyknow/" + "!{sys.executable} -m pip install git+https://github.com/nilp0inter/experta" ] }, { @@ -283,15 +282,15 @@ }, "outputs": [], "source": [ - "from pyknow import *\n", - "#import pyknow" + "from experta import *\n", + "#import experta" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Sistemimizi `KnowledgeEngine` sınıfını alt sınıf olarak tanımlayacağız. Her kural, ne zaman tetiklenmesi gerektiğini belirten `@Rule` anotasyonu ile ayrı bir fonksiyon olarak tanımlanır. Kuralın içinde, `declare` fonksiyonunu kullanarak yeni olgular ekleyebiliriz ve bu olguların eklenmesi, ileri çıkarım motoru tarafından bazı başka kuralların çağrılmasına neden olur.\n" + "Sistemimizi `KnowledgeEngine` sınıfından türeyen bir sınıf olarak tanımlayacağız. Her kural, ne zaman tetikleneceğini belirten `@Rule` anotasyonuyla ayrı bir fonksiyon olarak tanımlanır. Kuralın içinde, `declare` fonksiyonunu kullanarak yeni gerçekler ekleyebiliriz ve bu gerçeklerin eklenmesi, ileri çıkarım motoru tarafından bazı ek kuralların çağrılmasına yol açar.\n" ] }, { @@ -378,7 +377,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Bilgi tabanını tanımladıktan sonra, çalışma belleğimizi bazı başlangıç bilgileriyle doldururuz ve ardından çıkarım yapmak için `run()` metodunu çağırırız. Sonuç olarak, yeni çıkarılan bilgilerin çalışma belleğine eklendiğini görebilirsiniz, doğru başlangıç bilgilerini ayarladıysak hayvanla ilgili nihai bilgi de dahil.\n" + "Bir bilgi tabanı tanımladıktan sonra, çalışma belleğimizi bazı başlangıç gerçekleriyle doldururuz ve sonra çıkarım yapmak için `run()` metodunu çağırırız. Sonuç olarak, çalışma belleğine yeni çıkarılan gerçeklerin eklendiğini görebilirsiniz; bunlar hayvanla ilgili nihai gerçeği de içerir (eğer tüm başlangıç gerçeklerini doğru şekilde ayarladıysak).\n" ] }, { @@ -440,7 +439,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n---\n\n**Feragatname**: \nBu belge, AI çeviri hizmeti [Co-op Translator](https://github.com/Azure/co-op-translator) kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlık içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlamalar veya yanlış yorumlamalar için sorumluluk kabul etmiyoruz.\n" + "---\n\n\n**Feragatname**: \nBu belge, yapay zeka çeviri servisi [Co-op Translator](https://github.com/Azure/co-op-translator) kullanılarak çevrilmiştir. Doğruluk için özen gösterilse de, otomatik çevirilerin hatalar veya yanlışlıklar içerebileceğini lütfen unutmayın. Orijinal belge, kendi dilinde yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımı sonucu oluşabilecek yanlış anlamalar veya yorum hatalarından sorumlu değiliz.\n\n" ] } ], @@ -467,8 +466,8 @@ "version": "3.11.2" }, "coopTranslator": { - "original_hash": "ab2bd97b0453415b89a469284609a8ce", - "translation_date": "2025-08-28T13:35:35+00:00", + "original_hash": "8ef43db4b9182239fd150a76bd494fdb", + "translation_date": "2026-01-15T14:05:13+00:00", "source_file": "lessons/2-Symbolic/Animals.ipynb", "language_code": "tr" } diff --git a/translations/tr/lessons/2-Symbolic/README.md b/translations/tr/lessons/2-Symbolic/README.md index 5eebea40..3db3a3a8 100644 --- a/translations/tr/lessons/2-Symbolic/README.md +++ b/translations/tr/lessons/2-Symbolic/README.md @@ -1,116 +1,116 @@ # Bilgi Temsili ve Uzman Sistemler -![Sembolik AI içeriği özeti](../../../../translated_images/tr/ai-symbolic.715a30cb610411a6.webp) +![Sembolik AI içeriğinin özeti](../../../../../../translated_images/tr/ai-symbolic.715a30cb610411a6.webp) -> Sketchnote: [Tomomi Imura](https://twitter.com/girlie_mac) +> Sketchnote [Tomomi Imura](https://twitter.com/girlie_mac) tarafından -Yapay zeka arayışı, dünyayı insanlara benzer şekilde anlamlandırmak için bilgi arayışına dayanır. Peki bunu nasıl gerçekleştirebilirsiniz? +Yapay zekâ arayışı, insanların dünyayı anladığı gibi anlamak için bilgi arayışına dayanır. Peki bunu nasıl yapabilirsiniz? -## [Ders Öncesi Test](https://ff-quizzes.netlify.app/en/ai/quiz/3) +## [Ön-ders sınavı](https://ff-quizzes.netlify.app/en/ai/quiz/3) -Yapay zekanın ilk günlerinde, zeki sistemler oluşturmak için yukarıdan aşağıya yaklaşım (önceki derste tartışıldı) popülerdi. Fikir, insanlardan bilgiyi makine tarafından okunabilir bir forma çıkarmak ve ardından bunu otomatik olarak problem çözmek için kullanmaktı. Bu yaklaşım iki büyük fikre dayanıyordu: +AI'nin ilk dönemlerinde, akıllı sistemler yaratmaya yönelik yukarıdan aşağı yaklaşımı (önceki derste tartışılmıştır) popülerdi. Fikir, bilgiyi insanlardan makine tarafından okunabilir bir forma çıkarıp, ardından bunu otomatik problem çözmede kullanmaktı. Bu yaklaşım iki büyük fikre dayanıyordu: * Bilgi Temsili * Akıl Yürütme ## Bilgi Temsili -Sembolik AI'deki önemli kavramlardan biri **bilgi**dir. Bilgiyi *bilgi* veya *veri*den ayırmak önemlidir. Örneğin, kitapların bilgi içerdiğini söyleyebiliriz, çünkü kitapları inceleyerek uzmanlaşabiliriz. Ancak, kitapların içerdiği şey aslında *veri* olarak adlandırılır ve kitapları okuyarak bu veriyi dünya modelimize entegre ettiğimizde bu veriyi bilgiye dönüştürürüz. +Sembolik AI'deki önemli kavramlardan biri **bilgidir**. Bilgiyi *bilgi* veya *veri* den ayırmak önemlidir. Örneğin, kitapların bilgi içerdiği söylenebilir, çünkü kitaplar çalışılarak uzman olunur. Ancak kitapların içinde aslında *veri* vardır ve kitapları okuyup bu veriyi dünya modelimize entegre ederek bu veriyi bilgiye dönüştürürüz. -> ✅ **Bilgi**, kafamızda bulunan ve dünyayı anlama şeklimizi temsil eden bir şeydir. Aktif bir **öğrenme** süreciyle elde edilir ve aldığımız bilgileri aktif dünya modelimize entegre eder. +> ✅ **Bilgi**, kafamızda bulunan ve dünyayı anlama biçimimizi temsil eden bir şeydir. Edinilmesi, aldığımız bilgiler parçasını aktif olarak dünya modelimize entegre eden aktif bir **öğrenme** süreciyle olur. -Çoğu zaman bilgiyi kesin olarak tanımlamayız, ancak onu diğer ilgili kavramlarla [DIKW Piramidi](https://en.wikipedia.org/wiki/DIKW_pyramid) kullanarak hizalarız. Bu piramit şu kavramları içerir: +Çoğu zaman bilgiyi kesin tanımlamayız, ancak [DIKW Piramidi](https://en.wikipedia.org/wiki/DIKW_pyramid) kullanarak diğer ilgili kavramlarla ilişkilendiririz. İçerisinde şu kavramlar vardır: -* **Veri**, fiziksel medyada temsil edilen bir şeydir, örneğin yazılı metin veya konuşulan kelimeler. Veri, insanlardan bağımsız olarak var olur ve insanlar arasında aktarılabilir. -* **Bilgi**, veriyi kafamızda nasıl yorumladığımızdır. Örneğin, *bilgisayar* kelimesini duyduğumuzda, onun ne olduğunu anlamaya başlarız. -* **Bilgi**, bilginin dünya modelimize entegre edilmesidir. Örneğin, bir bilgisayarın ne olduğunu öğrendiğimizde, nasıl çalıştığı, maliyeti ve ne için kullanılabileceği hakkında fikirler ediniriz. Bu birbirine bağlı kavramlar ağı, bilgimizi oluşturur. -* **Bilgelik**, dünyayı anlamamızın bir başka seviyesidir ve *meta-bilgi*yi temsil eder, örneğin bilginin nasıl ve ne zaman kullanılacağına dair bir kavrayış. +* **Veri**, yazılı metin veya sözcükler gibi fiziksel ortamda temsil edilen bir şeydir. Veri, insanlardan bağımsız olarak var olur ve insanlar arasında aktarılabilir. +* **Bilgi**, veriyi kafamızda nasıl yorumladığımızdır. Örneğin, *bilgisayar* kelimesini duyduğumuzda ne olduğunu anlarız. +* **Bilgi** ise bilgilerin dünya modelimize entegre edilmesidir. Örneğin, bilgisayarın ne olduğunu öğrendiğimizde, nasıl çalıştığı, fiyatı ve kullanım alanları hakkındaki fikirlerimiz oluşur. Bu ağ biçimindeki ilişkili kavramlar bizim bilgimizi oluşturur. +* **Bilgelik**, dünyayı anlama düzeyimizde bir üst basamaktır ve *meta-bilgi*yi temsil eder, yani bilginin nasıl ve ne zaman kullanılacağına dair bir kavrayıştır. - + -*Resim [Wikipedia'dan](https://commons.wikimedia.org/w/index.php?curid=37705247), By Longlivetheux - Own work, CC BY-SA 4.0* +*Görsel [Vikipedi'den](https://commons.wikimedia.org/w/index.php?curid=37705247), By Longlivetheux - Own work, CC BY-SA 4.0* -Bu nedenle, **bilgi temsili** problemi, bilgiyi bir bilgisayar içinde veri şeklinde etkili bir şekilde temsil etmenin bir yolunu bulmaktır, böylece otomatik olarak kullanılabilir hale gelir. Bu bir spektrum olarak görülebilir: +Böylece, **bilgi temsili** problemi, bilgiyi otomatik olarak kullanılabilir hale getirmek için bilgisayar içinde etkili bir şekilde veri formunda temsil etme yollarını bulmaktır. Bu bir spektrum olarak görülebilir: -![Bilgi temsili spektrumu](../../../../translated_images/tr/knowledge-spectrum.b60df631852c0217.webp) +![Bilgi temsili spektrumu](../../../../../../translated_images/tr/knowledge-spectrum.b60df631852c0217.webp) -> Resim: [Dmitry Soshnikov](http://soshnikov.com) +> Görsel [Dmitry Soshnikov](http://soshnikov.com) tarafından -* Sol tarafta, bilgisayarlar tarafından etkili bir şekilde kullanılabilecek çok basit bilgi temsilleri vardır. En basit olanı algoritmik temsildir, bilgi bir bilgisayar programı ile temsil edilir. Ancak bu, bilginin temsil edilmesi için en iyi yol değildir, çünkü esnek değildir. Kafamızdaki bilgi genellikle algoritmik değildir. -* Sağ tarafta, doğal metin gibi temsiller vardır. Bu en güçlü olanıdır, ancak otomatik akıl yürütme için kullanılamaz. +* Solda, bilgisayarlar tarafından etkili biçimde kullanılabilen çok basit bilgi temsili türleri vardır. En basiti, bilgi bir bilgisayar programı tarafından temsil edildiğinde algoritmadır. Ancak bu, esnek olmadığı için bilginin temsilinde en iyi yol değildir. Kafamızdaki bilgi çoğunlukla algoritmik değildir. +* Sağda ise doğal metin gibi temsiller vardır. Bu en güçlüdür, ancak otomatik akıl yürütme için kullanılamaz. -> ✅ Bilgiyi kafanızda nasıl temsil ettiğinizi ve bunu notlara nasıl dönüştürdüğünüzü bir dakika düşünün. Hatırlamayı kolaylaştıran belirli bir format var mı? +> ✅ Bir dakika düşünün, bilginizi kafanızda nasıl temsil ediyor ve notlara dönüştürüyorsunuz. Hafızada kalmayı desteklemek için sizin için iyi çalışan özel bir format var mı? -## Bilgisayar Bilgi Temsillerini Sınıflandırma +## Bilgisayar Bilgi Temsillerinin Sınıflandırılması -Bilgisayar bilgi temsili yöntemlerini şu kategorilerde sınıflandırabiliriz: +Farklı bilgisayar bilgi temsili yöntemleri şu kategorilere ayrılabilir: -* **Ağ temsilleri**, kafamızda birbirine bağlı kavramlar ağı olduğu gerçeğine dayanır. Aynı ağları bir bilgisayar içinde bir grafik olarak yeniden oluşturabiliriz - **anlamsal ağ** olarak adlandırılır. +* **Ağ temsilleri**, kafamızda ilişkili kavramların bir ağ olarak var olduğu gerçeğine dayanır. Aynı ağları bilgisayar içinde, bir grafik olarak, yani **anlamsal ağ** olarak yeniden oluşturabiliriz. -1. **Nesne-Özellik-Değer üçlüleri** veya **özellik-değer çiftleri**. Bir grafik, bir bilgisayar içinde düğüm ve kenarların bir listesi olarak temsil edilebildiğinden, bir anlamsal ağı nesneler, özellikler ve değerler içeren bir üçlü listesiyle temsil edebiliriz. Örneğin, programlama dilleri hakkında şu üçlüleri oluşturabiliriz: +1. **Nesne-Özellik-Değer üçlüleri** veya **özellik-değer çiftleri**. Bir grafik bilgisayarda düğüm ve kenar listesi olarak temsil edilebildiğinden, anlamsal ağı, nesneler, özellikler ve değerlerden oluşan üçlük listesi şeklinde temsil edebiliriz. Örneğin, programlama dilleri hakkında şu üçlüleri oluşturabiliriz: Nesne | Özellik | Değer --------|-----------|------ -Python | is | Türsüz Dil +-------|---------|------ +Python | is | Untyped-Language Python | invented-by | Guido van Rossum -Python | block-syntax | girinti -Türsüz Dil | doesn't have | tür tanımları +Python | block-syntax | indentation +Untyped-Language | doesn't have | type definitions -> ✅ Üçlülerin diğer bilgi türlerini temsil etmek için nasıl kullanılabileceğini düşünün. +> ✅ Üçlüklerin diğer bilgi türlerini temsil etmek için nasıl kullanılabileceğini düşünün. -2. **Hiyerarşik temsiller**, kafamızda genellikle nesnelerin bir hiyerarşisini oluşturduğumuz gerçeğini vurgular. Örneğin, kanaryanın bir kuş olduğunu ve tüm kuşların kanatları olduğunu biliriz. Ayrıca kanaryanın genellikle ne renk olduğu ve uçuş hızının ne olduğu hakkında bir fikrimiz vardır. +2. **Hiyerarşik temsiller**, kafamızda nesnelerin hiyerarşisini sık oluşturduğumuzu vurgular. Örneğin, kanaryanın bir kuş olduğunu ve tüm kuşların kanatları olduğunu biliriz. Kanaryanın genellikle hangi renkte olduğunu ve uçuş hızını da az çok biliyoruz. - - **Çerçeve temsili**, her nesneyi veya nesne sınıfını **çerçeve** olarak temsil etmeye dayanır ve bu çerçeve **yuvalar** içerir. Yuvalar, olası varsayılan değerler, değer kısıtlamaları veya bir yuvanın değerini elde etmek için çağrılabilecek saklı prosedürler içerebilir. Tüm çerçeveler, nesne yönelimli programlama dillerindeki nesne hiyerarşisine benzer bir hiyerarşi oluşturur. - - **Senaryolar**, zaman içinde gelişebilecek karmaşık durumları temsil eden özel türde çerçevelerdir. + - **Çerçeve (frame) temsili**, her nesne veya nesne sınıfını **slot** (yuva) içeren bir **çerçeve** olarak temsil eder. Slotlar olası varsayılan değerler, değer kısıtlamaları ya da bir slotun değerini almak için çağrılabilecek prosedürler içerebilir. Tüm çerçeveler nesne yönelimli programlama dillerindeki nesne hiyerarşisine benzer bir hiyerarşi oluşturur. + - **Senaryolar (scenarios)**, zaman içinde gelişebilecek karmaşık durumları temsil eden özel tip çerçevelerdir. **Python** -Yuva | Değer | Varsayılan Değer | Aralık | +Slot | Değer | Varsayılan Değer | Aralık | -----|-------|------------------|--------| -Ad | Python | | | -Is-A | Türsüz Dil | | | -Değişken Durumu | | CamelCase | | -Program Uzunluğu | | | 5-5000 satır | -Blok Sözdizimi | Girinti | | | +Name | Python | | | +Is-A | Untyped-Language | | | +Variable Case | | CamelCase | | +Program Length | | | 5-5000 satır | +Block Syntax | Indent | | | -3. **Prosedürel temsiller**, belirli bir koşul meydana geldiğinde yürütülebilecek bir eylem listesiyle bilgiyi temsil etmeye dayanır. - - Üretim kuralları, sonuç çıkarmamıza olanak tanıyan if-then ifadeleridir. Örneğin, bir doktorun **EĞER** bir hastanın yüksek ateşi **VEYA** kan testinde yüksek C-reaktif protein seviyesi varsa **O ZAMAN** iltihaplanması olduğu şeklinde bir kuralı olabilir. Koşullardan birini karşılaştığımızda, iltihaplanma hakkında bir sonuca varabiliriz ve ardından bunu daha ileri akıl yürütmede kullanabiliriz. - - Algoritmalar, prosedürel temsillerin başka bir biçimi olarak kabul edilebilir, ancak bilgi tabanlı sistemlerde neredeyse hiç doğrudan kullanılmazlar. +3. **Prosedürel temsiller**, belirli bir koşul gerçekleştiğinde yürütülebilen eylemler listesini kullanarak bilgiyi temsil eder. + - Üretim kuralları, sonuç çıkarmamızı sağlayan if-then ifadeleridir. Örneğin, bir doktorun kuralı şöyle olabilir: **EĞER** hastada yüksek ateş **VEYA** kan testinde yüksek C-reaktif protein seviyesi varsa **O HALDE** inflamasyonu vardır. Koşullardan biri karşılandığında inflamasyon hakkında karar veririz ve bunu sonraki akıl yürütmede kullanırız. + - Algoritmalar prosedürel temsillerin bir başka şekli olarak kabul edilebilir, fakat bilgi tabanlı sistemlerde neredeyse hiç doğrudan kullanılmazlar. -4. **Mantık**, evrensel insan bilgisini temsil etmenin bir yolu olarak ilk kez Aristoteles tarafından önerilmiştir. - - Mantıksal Mantık, matematiksel bir teori olarak çok zengin olduğu için hesaplanabilir değildir, bu nedenle genellikle Prolog'da kullanılan Horn cümleleri gibi bir alt kümesi kullanılır. - - Tanımlayıcı Mantık, *anlamsal web* gibi nesne hiyerarşilerini ve dağıtılmış bilgi temsillerini temsil etmek ve akıl yürütmek için kullanılan mantıksal sistemler ailesidir. +4. **Mantık**, evrensel insan bilgisini temsil etmenin bir yolu olarak Aristoteles tarafından önerilmiştir. + - Önerme Mantığı hesaplanabilir olmayacak kadar zengindir, bu nedenle genellikle Prolog'ta kullanılan Horn kuralı gibi bir alt kümesi kullanılır. + - Betimleyici Mantık, *anlamsal web* gibi dağıtık bilgi temsillerinde nesne hiyerarşilerini temsil etmek ve akıl yürütmek için kullanılan mantıksal sistemler ailesidir. ## Uzman Sistemler -Sembolik AI'nın erken başarılarından biri, **uzman sistemler** olarak adlandırılan sistemlerdi - belirli bir problem alanında uzman gibi davranmak üzere tasarlanmış bilgisayar sistemleri. Bu sistemler, bir veya daha fazla insan uzmandan çıkarılan bir **bilgi tabanı**na dayanıyordu ve bunun üzerinde akıl yürütme yapan bir **çıkarım motoru** içeriyordu. +Sembolik AI'nin erken başarılarından biri olan **uzman sistemler**, sınırlı problem alanında bir uzman gibi davranmak üzere tasarlanmış bilgisayar sistemleridir. Bir veya daha fazla insan uzmandan çıkartılmış bir **bilgi tabanına** dayanır ve bunun üzerinde bazı akıl yürütme yapan bir **çıkarım motoruna** sahiptir. -![İnsan Mimarisi](../../../../translated_images/tr/arch-human.5d4d35f1bba3ab1c.webp) | ![Bilgi Tabanlı Sistem](../../../../translated_images/tr/arch-kbs.3ec5c150b09fa8da.webp) +![İnsan Mimarisi](../../../../../../translated_images/tr/arch-human.5d4d35f1bba3ab1c.webp) | ![Bilgi Tabanlı Sistem](../../../../../../translated_images/tr/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ -İnsan sinir sisteminin basitleştirilmiş yapısı | Bilgi tabanlı sistemin mimarisi +İnsan sinir sisteminin sadeleştirilmiş yapısı | Bilgi tabanlı sistem mimarisi -Uzman sistemler, insan akıl yürütme sistemine benzer şekilde inşa edilir, bu sistem **kısa süreli hafıza** ve **uzun süreli hafıza** içerir. Benzer şekilde, bilgi tabanlı sistemlerde şu bileşenleri ayırt ederiz: +Uzman sistemler, kısa dönem ve uzun dönem bellek içeren insan akıl yürütme sistemine benzer şekilde inşa edilir. Benzer şekilde bilgi tabanlı sistemlerde şu bileşenler ayırt edilir: -* **Problem hafızası**: Şu anda çözülmekte olan problemle ilgili bilgileri içerir, örneğin bir hastanın sıcaklığı veya kan basıncı, iltihaplanma olup olmadığı vb. Bu bilgiye **statik bilgi** de denir, çünkü şu anda problem hakkında bildiklerimizin bir anlık görüntüsünü içerir - *problem durumu* olarak adlandırılır. -* **Bilgi tabanı**: Bir problem alanı hakkında uzun süreli bilgiyi temsil eder. İnsan uzmanlardan manuel olarak çıkarılır ve danışmadan danışmaya değişmez. Çünkü bir problem durumundan diğerine geçiş yapmamıza olanak tanır, aynı zamanda **dinamik bilgi** olarak da adlandırılır. -* **Çıkarım motoru**: Problem durumu alanında arama sürecini yönlendirir, gerektiğinde kullanıcıya sorular sorar. Ayrıca her duruma uygulanacak doğru kuralları bulmaktan sorumludur. +* **Problem belleği**: Şu anda çözülen problem hakkında bilgi içerir, örneğin hastanın sıcaklığı veya tansiyonu, inflamasyon durumu vb. Bu bilgi **statik bilgi** olarak da adlandırılır, çünkü problem hakkında şu anda bildiklerimizin anlık bir görüntüsünü yani *problem durumu*nu içerir. +* **Bilgi tabanı**: Problem alanı hakkında uzun dönem bilgiyi temsil eder. İnsan uzmanlardan elle çıkarılır ve danışmanlıktan danışmanlığa değişmez. Bir problem durumu üzerinden diğerine geçişi sağladığı için **dinamik bilgi** olarak da adlandırılır. +* **Çıkarım motoru**: Problem durum uzayında arama sürecini yönetir, gerektiğinde kullanıcıdan sorular sorar. Her duruma uygulanacak doğru kuralları bulmaktan sorumludur. -Örneğin, fiziksel özelliklere dayanarak bir hayvanı belirleyen bir uzman sistemini ele alalım: +Örnek olarak, bir hayvanın fiziksel özelliklerine dayanarak tanımlandığı aşağıdaki uzman sistemi düşünelim: -![AND-OR Ağacı](../../../../translated_images/tr/AND-OR-Tree.5592d2c70187f283.webp) +![VEYA-VE ağacı](../../../../../../translated_images/tr/AND-OR-Tree.5592d2c70187f283.webp) -> Resim: [Dmitry Soshnikov](http://soshnikov.com) +> Görsel [Dmitry Soshnikov](http://soshnikov.com) tarafından -Bu diyagram **AND-OR ağacı** olarak adlandırılır ve üretim kurallarının grafiksel bir temsilidir. Uzmandan bilgi çıkarma sürecinin başında bir ağaç çizmek faydalıdır. Bilgiyi bilgisayar içinde temsil etmek için kuralları kullanmak daha uygundur: +Bu diyagram **AND-OR ağacı** olarak adlandırılır, üretim kurallarının grafiksel temsilidir. Uzmandan bilgi çıkarırken başta bunu çizmek faydalıdır. Bilgiyi bilgisayarda temsil etmek için kurallar kullanmak daha uygundur: ``` IF the animal eats meat @@ -121,78 +121,78 @@ OR (animal has sharp teeth THEN the animal is a carnivore ``` -Her kuralın sol tarafındaki koşul ve eylemin aslında nesne-özellik-değer (OÖD) üçlüleri olduğunu fark edebilirsiniz. **Çalışma hafızası**, şu anda çözülmekte olan problemle ilgili OÖD üçlülerini içerir. **Kural motoru**, bir koşulun karşılandığı kuralları arar ve bunları uygular, çalışma hafızasına başka bir üçlü ekler. +Daha ileri bakarsanız, kuralın sol tarafındaki her koşul ve eylem aslında nesne-özellik-değer (OÖD) üçlüleridir. **Çalışma belleği**, şu anda çözülen probleme ilişkin OÖD üçlülerini içerir. Bir **kural motoru**, koşul karşılanan kuralları arar ve uygular, çalışma belleğine yeni bir üçlü ekler. -> ✅ Hoşunuza giden bir konuda kendi AND-OR ağacınızı çizin! +> ✅ Kendi sevdiğiniz konuda bir AND-OR ağacı yazın! -### İleri ve Geri Çıkarım +### İleriye ve Geriye Çıkarım -Yukarıda açıklanan süreç **ileri çıkarım** olarak adlandırılır. Çalışma hafızasında problemle ilgili bazı başlangıç verileriyle başlar ve ardından şu akıl yürütme döngüsünü uygular: +Yukarıda açıklanan süreç **ileri çıkarım** olarak adlandırılır. Çalışma belleğinde mevcut olan problem hakkındaki bazı başlangıç verileri ile başlar ve şu akıl yürütme döngüsünü uygular: -1. Hedef özellik çalışma hafızasında mevcutsa - dur ve sonucu ver -2. Şu anda koşulu karşılanan tüm kuralları ara - **çatışma kümesi** elde et. -3. **Çatışma çözümü** yap - bu adımda uygulanacak bir kural seç. Farklı çatışma çözüm stratejileri olabilir: +1. Hedef özellik çalışma belleğinde varsa - dur ve sonucu ver +2. Koşulu şimdi karşılanan tüm kuralları ara - **çakışma kümesi** oluştur +3. **Çakışma çözümü** yap - bu adımda uygulanacak bir kural seç. Farklı çakışma çözüm stratejileri olabilir: - Bilgi tabanındaki ilk uygulanabilir kuralı seç - Rastgele bir kural seç - - *Daha spesifik* bir kural seç, yani "sol taraf"ta (LHS) en çok koşulu karşılayan kuralı seç -4. Seçilen kuralı uygula ve problem durumuna yeni bir bilgi parçası ekle -5. 1. adımdan tekrar et. + - *Daha spesifik* bir kural seç, yani sol taraftaki (LHS) en çok koşulu karşılayan +4. Seçilen kuralı uygula ve probleme yeni bir bilgi ekle +5. 1. adıma dön -Ancak, bazı durumlarda problem hakkında hiçbir bilgiye sahip olmadan başlayabilir ve bizi sonuca ulaştıracak sorular sorabiliriz. Örneğin, tıbbi teşhis yaparken, hastayı teşhis etmeye başlamadan önce tüm tıbbi analizleri önceden yapmayız. Bunun yerine, bir karar verilmesi gerektiğinde analiz yapmak isteriz. +Ancak bazen, problem hakkında bilgimizin boş olduğu durumlarda başlayıp, bizi sonuca ulaştıracak soruları sormak isteyebiliriz. Örneğin tıbbi tanı koyarken, hastayı teşhis etmeye başlamadan önce tüm testleri yapmayız. Karar verilmesi gereken zamanda testleri yaparız. -Bu süreç **geri çıkarım** kullanılarak modellenebilir. **Hedef** tarafından yönlendirilir - aradığımız hedef değeri: +Bu süreç **geri çıkarım** kullanılarak modellenebilir. Bu süreç, aradığımız özellik değeri olan **hedef** tarafından yönlendirilir: -1. Hedefin değerini verebilecek tüm kuralları seç (yani hedef sağ tarafta (RHS) olan kurallar) - bir çatışma kümesi -1. Bu özellik için hiçbir kural yoksa veya kullanıcıdan değeri sormamız gerektiğini söyleyen bir kural varsa - kullanıcıya sor, aksi takdirde: -1. Çatışma çözüm stratejisini kullanarak *hipotez* olarak kullanacağımız bir kural seç - bunu kanıtlamaya çalışacağız -1. Süreci, kuralın sol tarafındaki (LHS) tüm özellikler için tekrarlayarak onları hedef olarak kanıtlamaya çalış -1. Süreç herhangi bir noktada başarısız olursa - 3. adımda başka bir kural kullan. +1. Hedef değerini verebilecek tüm kuralları seç (örneğin hedef sağ tarafta (RHS), bir çakışma kümemiz var) +1. Bu özellik için kural yoksa veya kullanıcıdan değer istenmesi gerektiği söyleniyorsa - kullanıcıdan sor, yoksa: +1. Çakışma çözüm stratejisini kullanarak bir kural seç - bunu *varsayım* (hipotez) olarak kullanacağız +1. Kuralın sol tarafındaki tüm özellikler için süreci yinele, onları da hedef olarak kanıtlamaya çalış +1. Eğer süreç başarısız olursa - 3. adımda başka kural dene -> ✅ İleri çıkarımın daha uygun olduğu durumlar nelerdir? Peki ya geri çıkarım? +> ✅ Hangi durumlarda ileri çıkarım daha uygundur? Geriye çıkarım için ne dersiniz? -### Uzman Sistemleri Uygulama +### Uzman Sistemlerin Uygulanması -Uzman sistemler farklı araçlar kullanılarak uygulanabilir: +Uzman sistemler farklı araçlarla uygulanabilir: -* Yüksek seviyeli bir programlama dilinde doğrudan programlama. Bu en iyi fikir değildir, çünkü bilgi tabanlı bir sistemin ana avantajı, bilginin çıkarımdan ayrılmasıdır ve potansiyel olarak bir problem alanı uzmanı, çıkarım sürecinin ayrıntılarını anlamadan kurallar yazabilmelidir. -* **Uzman sistem kabuğu** kullanmak, yani bilgi temsili dili kullanarak bilgiyle doldurulmak üzere özel olarak tasarlanmış bir sistem. +* Doğrudan yüksek seviyeli bir programlama dilinde programlama yapılabilir. Bu en iyi fikir değildir çünkü bilgi tabanlı sistemin en önemli avantajı, bilginin çıkarımdan ayrılmasıdır ve potansiyel olarak problem alanı uzmanı çıkarım detaylarını anlamadan kuralları yazabilmelidir. +* **Uzman sistem kabuğu** kullanmak, yani bazı bilgi temsili dilleri içeren, bilgiyle doldurulmak üzere özel tasarlanmış bir sistem. -## ✍️ Egzersiz: Hayvan Çıkarımı +## ✍️ Alıştırma: Hayvan Çıkarımı -İleri ve geri çıkarım uzman sistemini uygulama örneği için [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) dosyasına bakın. +İleri ve geri çıkarım uzman sisteminin bir örneği için [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) dosyasına bakınız. -> **Not**: Bu örnek oldukça basittir ve bir uzman sisteminin nasıl göründüğüne dair bir fikir verir. Böyle bir sistem oluşturmaya başladığınızda, yalnızca belirli bir kural sayısına ulaştığınızda, yaklaşık 200+ kural, sistemin *zeki* davranışını fark etmeye başlarsınız. Bir noktada kurallar, hepsini akılda tutamayacak kadar karmaşık hale gelir ve bu noktada bir sistemin neden belirli kararlar verdiğini merak edebilirsiniz. Ancak, bilgi tabanlı sistemlerin önemli bir özelliği, alınan herhangi bir kararın tam olarak nasıl yapıldığını her zaman *açıklayabilmeniz*dir. +> **Not**: Bu örnek oldukça basittir ve sadece bir uzman sistemin nasıl göründüğüne dair fikir verir. Böyle bir sistem kurmaya başladığınızda ancak 200+ taneyle kural sayısına ulaşınca ancak *akıllı* davranış görmeye başlarsınız. Bir noktadan sonra kurallar çok karmaşık hale gelir ve sistemin neden belirli kararlar verdiği merak edilir. Ancak bilgi tabanlı sistemlerin önemli özelliği, verilen herhangi bir kararın nasıl verildiğinin her zaman tam olarak *açıklanabilir* olmasıdır. ## Ontolojiler ve Anlamsal Web -20. yüzyılın sonunda, bilgi temsili kullanarak internet kaynaklarını açıklamak için bir girişim vardı, böylece çok spesifik sorgulara karşılık gelen kaynakları bulmak mümkün olabilirdi. Bu hareket **Anlamsal Web** olarak adlandırıldı ve birkaç kavrama dayanıyordu: +20. yüzyılın sonunda, İnternet kaynaklarını belirli sorulara karşılık gelen kaynakları bulmayı mümkün kılacak şekilde açıklamak için bilgi temsili kullanma girişimi oldu. Bu hareket **Anlamsal Web** olarak adlandırıldı ve birkaç konsepte dayanıyordu: -- **[Tanımlayıcı mantıklar](https://en.wikipedia.org/wiki/Description_logic)** (DL) üzerine kurulu özel bir bilgi temsili. Çerçeve bilgi temsiline benzer, çünkü nesnelerin özellikleriyle bir hiyerarşi oluşturur, ancak resmi mantıksal semantiği ve çıkarımı vardır. DL'lerin bir ailesi vardır ve ifade gücü ile çıkarımın algoritmik karmaşıklığı arasında denge kurar. -- Tüm kavramların küresel bir URI tanımlayıcı ile temsil edildiği, interneti kapsayan bilgi hiyerarşileri oluşturmayı mümkün kılan dağıtılmış bilgi temsili. -- Bilgi tanımlama için XML tabanlı bir dil ailesi: RDF (Kaynak Tanımlama Çerçevesi), RDFS (RDF Şeması), OWL (Ontoloji Web Dili). +- **[Betimleyici mantık](https://en.wikipedia.org/wiki/Description_logic)** (DL) tabanlı özel bilgi temsili. Çerçeve bilgi temsiline benzer, çünkü nesne hiyerarşileri ve özellikleri oluşturur, ancak formal mantıksal anlamsal yapıya ve çıkarıma sahiptir. DL ailesi, anlatım gücü ile çıkarımın algoritmik karmaşıklığı arasında denge sağlar. +- Tüm kavramların küresel URI tanımlayıcıları ile temsil edildiği dağıtık bilgi temsili, internet çapında bilgi hiyerarşileri oluşturmayı mümkün kılar. +- Bilgi tanımlaması için XML tabanlı diller ailesi: RDF (Kaynak Tanımlama Çerçevesi), RDFS (RDF Şeması), OWL (Ontoloji Web Dili). -Semantik Web'in temel kavramlarından biri **Ontoloji** kavramıdır. Bu, bir problem alanını açık bir şekilde tanımlamak için bazı resmi bilgi temsilleri kullanılarak yapılan bir spesifikasyona işaret eder. En basit ontoloji, bir problem alanındaki nesnelerin bir hiyerarşisi olabilir, ancak daha karmaşık ontolojiler çıkarım yapmak için kullanılabilecek kuralları içerir. +Anlamsal Web'de temel bir kavram **Ontoloji** kavramıdır. Bu, bir problem alanının resmi bilgi temsili kullanılarak açıkça belirtilmesi anlamına gelir. En basit ontoloji, problem alanındaki nesnelerin bir hiyerarşisi olabilir, ancak daha karmaşık ontolojiler çıkarım için kullanılabilecek kuralları da içerecektir. -Semantik webde, tüm temsiller üçlüler üzerine kuruludur. Her nesne ve her ilişki URI ile benzersiz şekilde tanımlanır. Örneğin, bu AI Müfredatının Dmitry Soshnikov tarafından 1 Ocak 2022'de geliştirildiğini ifade etmek istersek, kullanabileceğimiz üçlüler şunlardır: +Anlamsal webde, tüm temsiller üçlüler üzerine kuruludur. Her nesne ve her ilişki URI ile benzersiz şekilde tanımlanır. Örneğin, bu AI Müfredatının 1 Ocak 2022'de Dmitry Soshnikov tarafından geliştirildiğini belirtmek istersek - kullanabileceğimiz üçlüler şunlardır: - + ``` -http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 13, 2007” +http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 1, 2022” http://github.com/microsoft/ai-for-beginners http://purl.org/dc/elements/1.1/creator http://soshnikov.com ``` -> ✅ Burada `http://www.example.com/terms/creation-date` ve `http://purl.org/dc/elements/1.1/creator` *yaratıcı* ve *oluşturma tarihi* kavramlarını ifade etmek için kullanılan, iyi bilinen ve evrensel olarak kabul edilen URI'lardır. +> ✅ Burada `http://www.example.com/terms/creation-date` ve `http://purl.org/dc/elements/1.1/creator` *oluşturan* ve *oluşturulma tarihi* kavramlarını ifade etmek için bilinen ve evrensel olarak kabul görmüş URI'lerdir. -Daha karmaşık bir durumda, bir yaratıcılar listesini tanımlamak istersek, RDF'de tanımlanan bazı veri yapılarını kullanabiliriz. +Daha karmaşık bir durumda, birden çok yaratıcıyı tanımlamak istersek, RDF'de tanımlı bazı veri yapıları kullanabiliriz. - + > Yukarıdaki diyagramlar [Dmitry Soshnikov](http://soshnikov.com) tarafından hazırlanmıştır. -Semantik Web'in geliştirilmesi, arama motorlarının ve metinden yapılandırılmış veri çıkarılmasını sağlayan doğal dil işleme tekniklerinin başarısı nedeniyle bir ölçüde yavaşladı. Ancak, bazı alanlarda ontolojileri ve bilgi tabanlarını korumak için hala önemli çabalar gösterilmektedir. Dikkate değer birkaç proje: +Anlamsal Web'in gelişimi, arama motorlarının ve metinden yapılandırılmış veriler çıkarmaya olanak sağlayan doğal dil işleme tekniklerinin başarısı nedeniyle bir ölçüde yavaşlamıştır. Ancak, bazı alanlarda ontolojileri ve bilgi tabanlarını korumak için hâlâ önemli çabalar vardır. Dikkate değer birkaç proje: -* [WikiData](https://wikidata.org/) Wikipedia ile ilişkili makine tarafından okunabilir bilgi tabanlarının bir koleksiyonudur. Verilerin çoğu, Wikipedia sayfalarındaki yapılandırılmış içerik parçaları olan *InfoBox*lardan çıkarılır. WikiData'yı Semantik Web için özel bir sorgu dili olan SPARQL ile [sorgulayabilirsiniz](https://query.wikidata.org/). İşte insanların en popüler göz renklerini gösteren örnek bir sorgu: +* [WikiData](https://wikidata.org/) Wikipedia ile ilişkilendirilen makine tarafından okunabilir bilgi tabanları koleksiyonudur. Verilerin çoğu Wikipedia *InfoBox*larından, Wikipedia sayfalarındaki yapılandırılmış içerik parçalarından çıkarılır. Wikidata'yı Anlamsal Web için özel sorgulama dili olan SPARQL ile [sorgulayabilirsiniz](https://query.wikidata.org/). İşte insanlar arasında en popüler göz renklerini gösteren örnek bir sorgu: ```sparql #defaultView:BubbleChart @@ -206,47 +206,51 @@ WHERE GROUP BY ?eyeColorLabel ``` -* [DBpedia](https://www.dbpedia.org/) WikiData'ya benzer başka bir girişimdir. +* [DBpedia](https://www.dbpedia.org/) WikiData'ya benzer başka bir projedir. -> ✅ Kendi ontolojilerinizi oluşturmayı veya mevcut olanları açmayı denemek isterseniz, [Protégé](https://protege.stanford.edu/) adlı harika bir görsel ontoloji düzenleyici var. İndirin veya çevrimiçi kullanın. +> ✅ Kendi ontolojilerinizi oluşturmayı ya da mevcut ontolojileri açmayı denemek isterseniz, [Protégé](https://protege.stanford.edu/) adlı harika bir görsel ontoloji editörü vardır. İndirin ya da çevrimiçi kullanın. - + -*Web Protégé düzenleyicisi Romanov Ailesi ontolojisi ile açık. Dmitry Soshnikov tarafından ekran görüntüsü.* +*Web Protégé editörü Romanov Ailesi ontolojisiyle açık. Ekran görüntüsü Dmitry Soshnikov tarafından alınmıştır.* ## ✍️ Alıştırma: Bir Aile Ontolojisi -Semantik Web tekniklerini kullanarak aile ilişkileri hakkında akıl yürütme örneği için [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) dosyasına bakın. Yaygın GEDCOM formatında temsil edilen bir aile ağacını ve aile ilişkileri ontolojisini alacağız ve belirli bir bireyler grubu için tüm aile ilişkilerinin bir grafiğini oluşturacağız. +Aile ilişkileri hakkında mantık yürütmek için Anlamsal Web tekniklerini kullanan bir örnek için [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) dosyasına bakın. Yaygın GEDCOM formatında temsil edilmiş bir soy ağacını ve aile ilişkileri ontolojisini alıp, belirli bireyler için tüm aile ilişkilerinin bir grafik temsilini oluşturacağız. -## Microsoft Concept Graph +## Microsoft Kavram Grafiği -Çoğu durumda, ontolojiler dikkatlice elle oluşturulur. Ancak, ontolojileri yapılandırılmamış verilerden, örneğin doğal dil metinlerinden **çıkarmak** da mümkündür. +Çoğu durumda ontolojiler özenle elle oluşturulur. Ancak, doğal dil metinlerinden örneğin yapılandırılmamış verilerden ontolojiler **çıkarılabilir** de. -Microsoft Research tarafından yapılan böyle bir girişim, [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste) ile sonuçlandı. +Microsoft Research tarafından yapılan bir çalışma sonucu ortaya çıkan [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste) buna örnektir. -Bu, `is-a` kalıtım ilişkisi kullanılarak bir araya getirilen büyük bir varlık koleksiyonudur. "Microsoft nedir?" gibi soruları yanıtlamayı sağlar - cevap, "bir şirket (olasılık 0.87) ve bir marka (olasılık 0.75)" gibi bir şey olabilir. +Bu, `is-a` kalıtım ilişkisi kullanılarak gruplandırılmış varlıkların büyük bir koleksiyonudur. “Microsoft nedir?” gibi sorulara yanıt vermeyi sağlar — örneğin “%0.87 olasılıkla bir şirket ve %0.75 olasılıkla bir marka” şeklinde. -Grafik, REST API olarak veya tüm varlık çiftlerini listeleyen büyük bir indirilebilir metin dosyası olarak kullanılabilir. +Graf, REST API olarak ya da tüm varlık çiftlerini listeleyen büyük indirilebilir bir metin dosyası olarak sunulur. ## ✍️ Alıştırma: Bir Kavram Grafiği -Microsoft Concept Graph'ı kullanarak haber makalelerini birkaç kategoriye ayırmayı görmek için [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) defterini deneyin. +Microsoft Concept Graph'ın haber makalelerini birkaç kategoriye nasıl ayırabileceğimizi görmek için [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) defterini deneyin. ## Sonuç -Günümüzde, yapay zeka genellikle *Makine Öğrenimi* veya *Sinir Ağları* ile eş anlamlı olarak kabul edilir. Ancak, bir insan aynı zamanda açık bir şekilde akıl yürütme sergiler, bu da şu anda sinir ağları tarafından ele alınmayan bir şeydir. Gerçek dünya projelerinde, açık akıl yürütme, açıklama gerektiren veya sistemin davranışını kontrollü bir şekilde değiştirme yeteneği gerektiren görevleri gerçekleştirmek için hala kullanılmaktadır. +Günümüzde, Yapay Zeka sıklıkla *Makine Öğrenimi* veya *Sinir Ağları* ile eş anlamlı olarak kabul edilir. Ancak, insan aynı zamanda açık mantık yürütme sergiler ve bu şu anda sinir ağları tarafından işlenemeyen bir şeydir. Gerçek dünya projelerinde, açık mantık yürütme halen açıklama gerektiren görevleri yerine getirmek ya da sistem davranışını kontrollü şekilde değiştirmek için kullanılır. -## 🚀 Meydan Okuma +## 🚀 Zorluk -Bu derse bağlı Aile Ontolojisi defterinde, diğer aile ilişkileriyle denemeler yapma fırsatı vardır. Aile ağacındaki insanlar arasında yeni bağlantılar keşfetmeyi deneyin. +Bu dersle ilişkili Aile Ontolojisi defterinde diğer aile ilişkileriyle de deney yapma fırsatı vardır. Soy ağacındaki kişiler arasında yeni bağlantılar keşfetmeye çalışın. -## [Ders sonrası test](https://ff-quizzes.netlify.app/en/ai/quiz/4) +## [Ders sonrası sınav](https://ff-quizzes.netlify.app/en/ai/quiz/4) -## Gözden Geçirme ve Kendi Kendine Çalışma +## Gözden Geçirme & Kendi Kendine Çalışma -İnsanların bilgiyi nicelleştirmeye ve kodlamaya çalıştığı alanları keşfetmek için internette araştırma yapın. Bloom'un Taksonomisine göz atın ve insanların dünyalarını anlamlandırmaya çalıştığı tarihsel süreçlere geri dönün. Linnaeus'un organizmalar için bir taksonomi oluşturma çalışmalarını inceleyin ve Dmitri Mendeleev'in kimyasal elementlerin tanımlanması ve gruplandırılması için bir yol yaratma biçimini gözlemleyin. Başka hangi ilginç örnekler bulabilirsiniz? +İnsanların bilgi nicelendirmeye ve kodlamaya çalıştığı alanları keşfetmek için internette araştırma yapın. Bloom'un Taksonomisine bakın ve insanların dünyalarını anlamaya nasıl çalıştığını tarihsel olarak inceleyin. Organizmaların taksonomisini oluşturmak için Linnaeus'un çalışmalarını keşfedin ve Dmitri Mendeleev'in kimyasal elementleri tanımlayıp gruplayacağı yolu gözlemleyin. Başka ne tür ilginç örnekler bulabilirsiniz? **Ödev**: [Bir Ontoloji Oluşturun](assignment.md) --- + +**Feragatname**: +Bu belge, [Co-op Translator](https://github.com/Azure/co-op-translator) adlı Yapay Zeka çeviri hizmeti kullanılarak çevrilmiştir. Doğruluk için çaba sarf etsek de, otomatik çevirilerin hatalar veya yanlışlıklar içerebileceğini lütfen unutmayınız. Orijinal belge, kendi ana dilindeki haliyle yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi tavsiye edilir. Bu çevirinin kullanılması nedeniyle oluşabilecek herhangi bir yanlış anlama veya yorum hatasından sorumlu değiliz. + \ No newline at end of file